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['Eli Schwartz', 'Raja Giryes', 'Alex M. Bronstein']
1801.06724v1
We present DeepISP, a full end-to-end deep neural model of the camera image signal processing (ISP) pipeline. Our model learns a mapping from the raw low-light mosaiced image to the final visually compelling image and encompasses low-level tasks such as demosaicing and denoising as well as higher-level tasks such as color correction and image adjustment. The training and evaluation of the pipeline was performed on a dedicated dataset containing pairs of low-light and well-lit images captured by a Samsung S7 smartphone camera in both raw and processed JPEG formats. The proposed solution achieves state-of-the-art performance in objective evaluation of PSNR on the subtask of joint denoising and demosaicing. For the full end-to-end pipeline, it achieves better visual quality compared to the manufacturer ISP, in both a subjective human assessment and when rated by a deep model trained for assessing image quality.
DeepISP: Learning End-to-End Image Processing Pipeline
2,018
http://arxiv.org/pdf/1801.06724v1
Title DeepISP Learning EndtoEnd Image Processing Pipeline Summary present DeepISP full endtoend deep neural model camera image signal processing ISP pipeline model learns mapping raw lowlight mosaiced image final visually compelling image encompasses lowlevel task demosaicing denoising well higherlevel task color correction image adjustment training evaluation pipeline performed dedicated dataset containing pair lowlight welllit image captured Samsung S7 smartphone camera raw processed JPEG format proposed solution achieves stateoftheart performance objective evaluation PSNR subtask joint denoising demosaicing full endtoend pipeline achieves better visual quality compared manufacturer ISP subjective human assessment rated deep model trained assessing image quality Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,201
40,201
['Lequan Yu', 'Xianzhi Li', 'Chi-Wing Fu', 'Daniel Cohen-Or', 'Pheng-Ann Heng']
1801.06761v1
Learning and analyzing 3D point cloud with deep networks is challenging due to the sparseness and irregularity of the data. In this paper, we present a data-driven point cloud upsampling technique. The key idea is to learn multi-level features per point, and then expanding them via a multi-branch convolution unit, to implicitly expand the point set in feature space. The expanded feature is then split to a multitude of features, which are then reconstructed to an upsampled point set. Our network is applied at a patch-level, with a joint loss function that encourages the upsampled points to remain on the underlying surface with a uniform distribution. We conduct various experiments using synthesis and scan data to evaluate our method and demonstrate its superiority over some baseline methods and an optimization-based method. The results show that our upsampled results have better uniformity, and sampled closer to the underlying surface.
PU-Net: Point Cloud Upsampling Network
2,018
http://arxiv.org/pdf/1801.06761v1
Title PUNet Point Cloud Upsampling Network Summary Learning analyzing 3D point cloud deep network challenging due sparseness irregularity data paper present datadriven point cloud upsampling technique key idea learn multilevel feature per point expanding via multibranch convolution unit implicitly expand point set feature space expanded feature split multitude feature reconstructed upsampled point set network applied patchlevel joint loss function encourages upsampled point remain underlying surface uniform distribution conduct various experiment using synthesis scan data evaluate method demonstrate superiority baseline method optimizationbased method result show upsampled result better uniformity sampled closer underlying surface Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,202
40,202
['Luis Herranz', 'Weiqing Min', 'Shuqiang Jiang']
1801.07239v1
The central role of food in our individual and social life, combined with recent technological advances, has motivated a growing interest in applications that help to better monitor dietary habits as well as the exploration and retrieval of food-related information. We review how visual content, context and external knowledge can be integrated effectively into food-oriented applications, with special focus on recipe analysis and retrieval, food recommendation, and the restaurant context as emerging directions.
Food recognition and recipe analysis: integrating visual content, context and external knowledge
2,018
http://arxiv.org/pdf/1801.07239v1
Title Food recognition recipe analysis integrating visual content context external knowledge Summary central role food individual social life combined recent technological advance motivated growing interest application help better monitor dietary habit well exploration retrieval foodrelated information review visual content context external knowledge integrated effectively foodoriented application special focus recipe analysis retrieval food recommendation restaurant context emerging direction Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,203
40,203
['Zeyuan Chen', 'Shaoliang Nie', 'Tianfu Wu', 'Christopher G. Healey']
1801.07632v1
We present a deep learning approach for high resolution face completion with multiple controllable attributes (e.g., male and smiling) under arbitrary masks. Face completion entails understanding both structural meaningfulness and appearance consistency locally and globally to fill in "holes" whose content do not appear elsewhere in an input image. It is a challenging task with the difficulty level increasing significantly with respect to high resolution, the complexity of "holes" and the controllable attributes of filled-in fragments. Our system addresses the challenges by learning a fully end-to-end framework that trains generative adversarial networks (GANs) progressively from low resolution to high resolution with conditional vectors encoding controllable attributes. We design novel network architectures to exploit information across multiple scales effectively and efficiently. We introduce new loss functions encouraging sharp completion. We show that our system can complete faces with large structural and appearance variations using a single feed-forward pass of computation with mean inference time of 0.007 seconds for images at 1024 x 1024 resolution. We also perform a pilot human study that shows our approach outperforms state-of-the-art face completion methods in terms of rank analysis. The code will be released upon publication.
High Resolution Face Completion with Multiple Controllable Attributes via Fully End-to-End Progressive Generative Adversarial Networks
2,018
http://arxiv.org/pdf/1801.07632v1
Title High Resolution Face Completion Multiple Controllable Attributes via Fully EndtoEnd Progressive Generative Adversarial Networks Summary present deep learning approach high resolution face completion multiple controllable attribute eg male smiling arbitrary mask Face completion entail understanding structural meaningfulness appearance consistency locally globally fill hole whose content appear elsewhere input image challenging task difficulty level increasing significantly respect high resolution complexity hole controllable attribute filledin fragment system address challenge learning fully endtoend framework train generative adversarial network GANs progressively low resolution high resolution conditional vector encoding controllable attribute design novel network architecture exploit information across multiple scale effectively efficiently introduce new loss function encouraging sharp completion show system complete face large structural appearance variation using single feedforward pas computation mean inference time 0007 second image 1024 x 1024 resolution also perform pilot human study show approach outperforms stateoftheart face completion method term rank analysis code released upon publication Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,204
40,204
['Jiahui Yu', 'Zhe Lin', 'Jimei Yang', 'Xiaohui Shen', 'Xin Lu', 'Thomas S. Huang']
1801.07892v2
Recent deep learning based approaches have shown promising results for the challenging task of inpainting large missing regions in an image. These methods can generate visually plausible image structures and textures, but often create distorted structures or blurry textures inconsistent with surrounding areas. This is mainly due to ineffectiveness of convolutional neural networks in explicitly borrowing or copying information from distant spatial locations. On the other hand, traditional texture and patch synthesis approaches are particularly suitable when it needs to borrow textures from the surrounding regions. Motivated by these observations, we propose a new deep generative model-based approach which can not only synthesize novel image structures but also explicitly utilize surrounding image features as references during network training to make better predictions. The model is a feed-forward, fully convolutional neural network which can process images with multiple holes at arbitrary locations and with variable sizes during the test time. Experiments on multiple datasets including faces (CelebA, CelebA-HQ), textures (DTD) and natural images (ImageNet, Places2) demonstrate that our proposed approach generates higher-quality inpainting results than existing ones. Code, demo and models are available at: https://github.com/JiahuiYu/generative_inpainting.
Generative Image Inpainting with Contextual Attention
2,018
http://arxiv.org/pdf/1801.07892v2
Title Generative Image Inpainting Contextual Attention Summary Recent deep learning based approach shown promising result challenging task inpainting large missing region image method generate visually plausible image structure texture often create distorted structure blurry texture inconsistent surrounding area mainly due ineffectiveness convolutional neural network explicitly borrowing copying information distant spatial location hand traditional texture patch synthesis approach particularly suitable need borrow texture surrounding region Motivated observation propose new deep generative modelbased approach synthesize novel image structure also explicitly utilize surrounding image feature reference network training make better prediction model feedforward fully convolutional neural network process image multiple hole arbitrary location variable size test time Experiments multiple datasets including face CelebA CelebAHQ texture DTD natural image ImageNet Places2 demonstrate proposed approach generates higherquality inpainting result existing one Code demo model available httpsgithubcomJiahuiYugenerativeinpainting Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,205
40,205
['Kushal Kafle', 'Scott Cohen', 'Brian Price', 'Christopher Kanan']
1801.08163v1
Bar charts are an effective way for humans to convey information to each other, but today's algorithms cannot parse them. Existing methods fail when faced with minor variations in appearance. Here, we present DVQA, a dataset that tests many aspects of bar chart understanding in a question answering framework. Unlike visual question answering (VQA), DVQA requires processing words and answers that are unique to a particular bar chart. State-of-the-art VQA algorithms perform poorly on DVQA, and we propose two strong baselines that perform considerably better. Our work will enable algorithms to automatically extract semantic information from vast quantities of literature in science, business, and other areas.
DVQA: Understanding Data Visualizations via Question Answering
2,018
http://arxiv.org/pdf/1801.08163v1
Title DVQA Understanding Data Visualizations via Question Answering Summary Bar chart effective way human convey information today algorithm cannot parse Existing method fail faced minor variation appearance present DVQA dataset test many aspect bar chart understanding question answering framework Unlike visual question answering VQA DVQA requires processing word answer unique particular bar chart Stateoftheart VQA algorithm perform poorly DVQA propose two strong baseline perform considerably better work enable algorithm automatically extract semantic information vast quantity literature science business area Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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['Utsav B. Gewali', 'Sildomar T. Monteiro']
1801.08268v1
Undirected graphical models have been successfully used to jointly model the spatial and the spectral dependencies in earth observing hyperspectral images. They produce less noisy, smooth, and spatially coherent land cover maps and give top accuracies on many datasets. Moreover, they can easily be combined with other state-of-the-art approaches, such as deep learning. This has made them an essential tool for remote sensing researchers and practitioners. However, graphical models have not been easily accessible to the larger remote sensing community as they are not discussed in standard remote sensing textbooks and not included in the popular remote sensing software and toolboxes. In this tutorial, we provide a theoretical introduction to Markov random fields and conditional random fields based spatial-spectral classification for land cover mapping along with a detailed step-by-step practical guide on applying these methods using freely available software. Furthermore, the discussed methods are benchmarked on four public hyperspectral datasets for a fair comparison among themselves and easy comparison with the vast number of methods in literature which use the same datasets. The source code necessary to reproduce all the results in the paper is published on-line to make it easier for the readers to apply these techniques to different remote sensing problems.
A Tutorial on Modeling and Inference in Undirected Graphical Models for Hyperspectral Image Analysis
2,018
http://arxiv.org/pdf/1801.08268v1
Title Tutorial Modeling Inference Undirected Graphical Models Hyperspectral Image Analysis Summary Undirected graphical model successfully used jointly model spatial spectral dependency earth observing hyperspectral image produce le noisy smooth spatially coherent land cover map give top accuracy many datasets Moreover easily combined stateoftheart approach deep learning made essential tool remote sensing researcher practitioner However graphical model easily accessible larger remote sensing community discussed standard remote sensing textbook included popular remote sensing software toolbox tutorial provide theoretical introduction Markov random field conditional random field based spatialspectral classification land cover mapping along detailed stepbystep practical guide applying method using freely available software Furthermore discussed method benchmarked four public hyperspectral datasets fair comparison among easy comparison vast number method literature use datasets source code necessary reproduce result paper published online make easier reader apply technique different remote sensing problem Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,207
40,207
['Lloyd H. Hughes', 'Michael Schmitt', 'Lichao Mou', 'Yuanyuan Wang', 'Xiao Xiang Zhu']
1801.08467v1
In this letter, we propose a pseudo-siamese convolutional neural network (CNN) architecture that enables to solve the task of identifying corresponding patches in very-high-resolution (VHR) optical and synthetic aperture radar (SAR) remote sensing imagery. Using eight convolutional layers each in two parallel network streams, a fully connected layer for the fusion of the features learned in each stream, and a loss function based on binary cross-entropy, we achieve a one-hot indication if two patches correspond or not. The network is trained and tested on an automatically generated dataset that is based on a deterministic alignment of SAR and optical imagery via previously reconstructed and subsequently co-registered 3D point clouds. The satellite images, from which the patches comprising our dataset are extracted, show a complex urban scene containing many elevated objects (i.e. buildings), thus providing one of the most difficult experimental environments. The achieved results show that the network is able to predict corresponding patches with high accuracy, thus indicating great potential for further development towards a generalized multi-sensor key-point matching procedure. Index Terms-synthetic aperture radar (SAR), optical imagery, data fusion, deep learning, convolutional neural networks (CNN), image matching, deep matching
Identifying Corresponding Patches in SAR and Optical Images with a Pseudo-Siamese CNN
2,018
http://arxiv.org/pdf/1801.08467v1
Title Identifying Corresponding Patches SAR Optical Images PseudoSiamese CNN Summary letter propose pseudosiamese convolutional neural network CNN architecture enables solve task identifying corresponding patch veryhighresolution VHR optical synthetic aperture radar SAR remote sensing imagery Using eight convolutional layer two parallel network stream fully connected layer fusion feature learned stream loss function based binary crossentropy achieve onehot indication two patch correspond network trained tested automatically generated dataset based deterministic alignment SAR optical imagery via previously reconstructed subsequently coregistered 3D point cloud satellite image patch comprising dataset extracted show complex urban scene containing many elevated object ie building thus providing one difficult experimental environment achieved result show network able predict corresponding patch high accuracy thus indicating great potential development towards generalized multisensor keypoint matching procedure Index Termssynthetic aperture radar SAR optical imagery data fusion deep learning convolutional neural network CNN image matching deep matching Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,208
40,208
['Prem Sewak Sudhish']
1801.08480v1
The abstract of the thesis consists of three sections, videlicet, Motivation Chapter Organization Salient Contributions. The complete abstract is included with the thesis. The final section on Salient Contributions is reproduced below. Salient Contributions The research presents the following salient contributions: i. A novel technique has been developed for comparing biographical information, by combining the average impact of Levenshtein, Damerau-Levenshtein, and editor distances. The impact is calculated as the ratio of the edit distance to the maximum possible edit distance between two strings of the same lengths as the given pair of strings. This impact lies in the range [0, 1] and can easily be converted to a similarity (matching) score by subtracting the impact from unity. ii. A universal soft computing framework is proposed for adaptively fusing biometric and biographical information by making real-time decisions to determine after consideration of each individual identifier whether computation of matching scores and subsequent fusion of additional identifiers, including biographical information is required. This proposed framework not only improves the accuracy of the system by fusing less reliable information (e.g. biographical information) only for instances where such a fusion is required, but also improves the efficiency of the system by computing matching scores for various available identifiers only when this computation is considered necessary. iii. A scientific method for comparing efficiency of fusion strategies through a predicted effort to error trade-off curve.
An Integrated Soft Computing Approach to a Multi-biometric Security Model
2,018
http://arxiv.org/pdf/1801.08480v1
Title Integrated Soft Computing Approach Multibiometric Security Model Summary abstract thesis consists three section videlicet Motivation Chapter Organization Salient Contributions complete abstract included thesis final section Salient Contributions reproduced Salient Contributions research present following salient contribution novel technique developed comparing biographical information combining average impact Levenshtein DamerauLevenshtein editor distance impact calculated ratio edit distance maximum possible edit distance two string length given pair string impact lie range 0 1 easily converted similarity matching score subtracting impact unity ii universal soft computing framework proposed adaptively fusing biometric biographical information making realtime decision determine consideration individual identifier whether computation matching score subsequent fusion additional identifier including biographical information required proposed framework improves accuracy system fusing le reliable information eg biographical information instance fusion required also improves efficiency system computing matching score various available identifier computation considered necessary iii scientific method comparing efficiency fusion strategy predicted effort error tradeoff curve Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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['Morteza Daneshmand', 'Ahmed Helmi', 'Egils Avots', 'Fatemeh Noroozi', 'Fatih Alisinanoglu', 'Hasan Sait Arslan', 'Jelena Gorbova', 'Rain Eric Haamer', 'Cagri Ozcinar', 'Gholamreza Anbarjafari']
1801.08863v1
This paper provides an overview of 3D scanning methodologies and technologies proposed in the existing scientific and industrial literature. Throughout the paper, various types of the related techniques are reviewed, which consist, mainly, of close-range, aerial, structure-from-motion and terrestrial photogrammetry, and mobile, terrestrial and airborne laser scanning, as well as time-of-flight, structured-light and phase-comparison methods, along with comparative and combinational studies, the latter being intended to help make a clearer distinction on the relevance and reliability of the possible choices. Moreover, outlier detection and surface fitting procedures are discussed concisely, which are necessary post-processing stages.
3D Scanning: A Comprehensive Survey
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http://arxiv.org/pdf/1801.08863v1
Title 3D Scanning Comprehensive Survey Summary paper provides overview 3D scanning methodology technology proposed existing scientific industrial literature Throughout paper various type related technique reviewed consist mainly closerange aerial structurefrommotion terrestrial photogrammetry mobile terrestrial airborne laser scanning well timeofflight structuredlight phasecomparison method along comparative combinational study latter intended help make clearer distinction relevance reliability possible choice Moreover outlier detection surface fitting procedure discussed concisely necessary postprocessing stage Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,210
['Mikhail Startsev', 'Michael Dorr']
1801.08925v2
Predicting attention is a popular topic at the intersection of human and computer vision, but video saliency prediction has only recently begun to benefit from deep learning-based approaches. Even though most of the available video-based saliency data sets and models claim to target human observers' fixations, they fail to differentiate them from smooth pursuit (SP), a major eye movement type that is unique to perception of dynamic scenes. In this work, we aim to make this distinction explicit, to which end we (i) use both algorithmic and manual annotations of SP traces and other eye movements for two well-established video saliency data sets, (ii) train Slicing Convolutional Neural Networks (S-CNN) for saliency prediction on either fixation- or SP-salient locations, and (iii) evaluate ours and over 20 popular published saliency models on the two annotated data sets for predicting both SP and fixations, as well as on another data set of human fixations. Our proposed model, trained on an independent set of videos, outperforms the state-of-the-art saliency models in the task of SP prediction on all considered data sets. Moreover, this model also demonstrates superior performance in the prediction of "classical" fixation-based saliency. Our results emphasize the importance of selectively approaching training set construction for attention modelling.
Supersaliency: Predicting Smooth Pursuit-Based Attention with Slicing CNNs Improves Fixation Prediction for Naturalistic Videos
2,018
http://arxiv.org/pdf/1801.08925v2
Title Supersaliency Predicting Smooth PursuitBased Attention Slicing CNNs Improves Fixation Prediction Naturalistic Videos Summary Predicting attention popular topic intersection human computer vision video saliency prediction recently begun benefit deep learningbased approach Even though available videobased saliency data set model claim target human observer fixation fail differentiate smooth pursuit SP major eye movement type unique perception dynamic scene work aim make distinction explicit end use algorithmic manual annotation SP trace eye movement two wellestablished video saliency data set ii train Slicing Convolutional Neural Networks SCNN saliency prediction either fixation SPsalient location iii evaluate 20 popular published saliency model two annotated data set predicting SP fixation well another data set human fixation proposed model trained independent set video outperforms stateoftheart saliency model task SP prediction considered data set Moreover model also demonstrates superior performance prediction classical fixationbased saliency result emphasize importance selectively approaching training set construction attention modelling Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,211
40,211
['Aaditya Prakash', 'Nick Moran', 'Solomon Garber', 'Antonella DiLillo', 'James Storer']
1801.08926v2
CNNs are poised to become integral parts of many critical systems. Despite their robustness to natural variations, image pixel values can be manipulated, via small, carefully crafted, imperceptible perturbations, to cause a model to misclassify images. We present an algorithm to process an image so that classification accuracy is significantly preserved in the presence of such adversarial manipulations. Image classifiers tend to be robust to natural noise, and adversarial attacks tend to be agnostic to object location. These observations motivate our strategy, which leverages model robustness to defend against adversarial perturbations by forcing the image to match natural image statistics. Our algorithm locally corrupts the image by redistributing pixel values via a process we term pixel deflection. A subsequent wavelet-based denoising operation softens this corruption, as well as some of the adversarial changes. We demonstrate experimentally that the combination of these techniques enables the effective recovery of the true class, against a variety of robust attacks. Our results compare favorably with current state-of-the-art defenses, without requiring retraining or modifying the CNN.
Deflecting Adversarial Attacks with Pixel Deflection
2,018
http://arxiv.org/pdf/1801.08926v2
Title Deflecting Adversarial Attacks Pixel Deflection Summary CNNs poised become integral part many critical system Despite robustness natural variation image pixel value manipulated via small carefully crafted imperceptible perturbation cause model misclassify image present algorithm process image classification accuracy significantly preserved presence adversarial manipulation Image classifier tend robust natural noise adversarial attack tend agnostic object location observation motivate strategy leverage model robustness defend adversarial perturbation forcing image match natural image statistic algorithm locally corrupts image redistributing pixel value via process term pixel deflection subsequent waveletbased denoising operation softens corruption well adversarial change demonstrate experimentally combination technique enables effective recovery true class variety robust attack result compare favorably current stateoftheart defense without requiring retraining modifying CNN Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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['Michael Tanner', 'Stefan Saftescu', 'Alex Bewley', 'Paul Newman']
1801.09128v1
Dense reconstructions often contain errors that prior work has so far minimised using high quality sensors and regularising the output. Nevertheless, errors still persist. This paper proposes a machine learning technique to identify errors in three dimensional (3D) meshes. Beyond simply identifying errors, our method quantifies both the magnitude and the direction of depth estimate errors when viewing the scene. This enables us to improve the reconstruction accuracy. We train a suitably deep network architecture with two 3D meshes: a high-quality laser reconstruction, and a lower quality stereo image reconstruction. The network predicts the amount of error in the lower quality reconstruction with respect to the high-quality one, having only view the former through its input. We evaluate our approach by correcting two-dimensional (2D) inverse-depth images extracted from the 3D model, and show that our method improves the quality of these depth reconstructions by up to a relative 10% RMSE.
Meshed Up: Learnt Error Correction in 3D Reconstructions
2,018
http://arxiv.org/pdf/1801.09128v1
Title Meshed Learnt Error Correction 3D Reconstructions Summary Dense reconstruction often contain error prior work far minimised using high quality sensor regularising output Nevertheless error still persist paper proposes machine learning technique identify error three dimensional 3D mesh Beyond simply identifying error method quantifies magnitude direction depth estimate error viewing scene enables u improve reconstruction accuracy train suitably deep network architecture two 3D mesh highquality laser reconstruction lower quality stereo image reconstruction network predicts amount error lower quality reconstruction respect highquality one view former input evaluate approach correcting twodimensional 2D inversedepth image extracted 3D model show method improves quality depth reconstruction relative 10 RMSE Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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['Vishwanath Saragadam', 'Aswin C. Sankaranarayanan']
1801.09343v1
Low-rank modeling of hyperspectral images has found extensive use in numerous inference tasks. In this paper, we present an adaptive imaging technique that optically computes a low-rank representation of the scene's hyperspectral image. We make significant contributions towards simultaneously highly resolvable spectral and spatial measurements by introducing pupil coding. The proposed imager, KRISM, provides optical implementation of two operators on the scene's hyperspectral image --- namely, a spectrally-coded spatial measurement and a spatially-coded spectral measurement. By iterating between the two operators, using the output of one as the input to the other, we show that the top singular vectors and singular values of a hyperspectral image can be computed in the optical domain with very few measurements. We present an optical setup and show several compelling real world examples that demonstrate the effectiveness of our proposed algorithm.
KRISM --- Krylov Subspace-based Optical Computing of Hyperspectral Images
2,018
http://arxiv.org/pdf/1801.09343v1
Title KRISM Krylov Subspacebased Optical Computing Hyperspectral Images Summary Lowrank modeling hyperspectral image found extensive use numerous inference task paper present adaptive imaging technique optically computes lowrank representation scene hyperspectral image make significant contribution towards simultaneously highly resolvable spectral spatial measurement introducing pupil coding proposed imager KRISM provides optical implementation two operator scene hyperspectral image namely spectrallycoded spatial measurement spatiallycoded spectral measurement iterating two operator using output one input show top singular vector singular value hyperspectral image computed optical domain measurement present optical setup show several compelling real world example demonstrate effectiveness proposed algorithm Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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-0.040111467242240906, -0.04410405457019806, -0.046859707683324814, -0.0016392493853345513, -0.010765223763883114, -0.054596275091171265, 0.011397046968340874]
40,214
40,214
['Hiroya Maeda', 'Yoshihide Sekimoto', 'Toshikazu Seto', 'Takehiro Kashiyama', 'Hiroshi Omata']
1801.09454v2
Research on damage detection of road surfaces using image processing techniques has been actively conducted, achieving considerably high detection accuracies. Many studies only focus on the detection of the presence or absence of damage. However, in a real-world scenario, when the road managers from a governing body need to repair such damage, they need to clearly understand the type of damage in order to take effective action. In addition, in many of these previous studies, the researchers acquire their own data using different methods. Hence, there is no uniform road damage dataset available openly, leading to the absence of a benchmark for road damage detection. This study makes three contributions to address these issues. First, to the best of our knowledge, for the first time, a large-scale road damage dataset is prepared. This dataset is composed of 9,053 road damage images captured with a smartphone installed on a car, with 15,435 instances of road surface damage included in these road images. In order to generate this dataset, we cooperated with 7 municipalities in Japan and acquired road images for more than 40 hours. These images were captured in a wide variety of weather and illuminance conditions. In each image, we annotated the bounding box representing the location and type of damage. Next, we used a state-of-the-art object detection method using convolutional neural networks to train the damage detection model with our dataset, and compared the accuracy and runtime speed on both, using a GPU server and a smartphone. Finally, we demonstrate that the type of damage can be classified into eight types with high accuracy by applying the proposed object detection method. The road damage dataset, our experimental results, and the developed smartphone application used in this study are publicly available (https://github.com/sekilab/RoadDamageDetector/).
Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone
2,018
http://arxiv.org/pdf/1801.09454v2
Title Road Damage Detection Using Deep Neural Networks Images Captured Smartphone Summary Research damage detection road surface using image processing technique actively conducted achieving considerably high detection accuracy Many study focus detection presence absence damage However realworld scenario road manager governing body need repair damage need clearly understand type damage order take effective action addition many previous study researcher acquire data using different method Hence uniform road damage dataset available openly leading absence benchmark road damage detection study make three contribution address issue First best knowledge first time largescale road damage dataset prepared dataset composed 9053 road damage image captured smartphone installed car 15435 instance road surface damage included road image order generate dataset cooperated 7 municipality Japan acquired road image 40 hour image captured wide variety weather illuminance condition image annotated bounding box representing location type damage Next used stateoftheart object detection method using convolutional neural network train damage detection model dataset compared accuracy runtime speed using GPU server smartphone Finally demonstrate type damage classified eight type high accuracy applying proposed object detection method road damage dataset experimental result developed smartphone application used study publicly available httpsgithubcomsekilabRoadDamageDetector Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,215
['AmirAbbas Davari', 'Nikolaos Sakaltras', 'Armin Haeberle', 'Sulaiman Vesal', 'Vincent Christlein', 'Andreas Maier', 'Christian Riess']
1801.09472v1
Old master drawings were mostly created step by step in several layers using different materials. To art historians and restorers, examination of these layers brings various insights into the artistic work process and helps to answer questions about the object, its attribution and its authenticity. However, these layers typically overlap and are oftentimes difficult to differentiate with the unaided eye. For example, a common layer combination is red chalk under ink. In this work, we propose an image processing pipeline that operates on hyperspectral images to separate such layers. Using this pipeline, we show that hyperspectral images enable better layer separation than RGB images, and that spectral focus stacking aids the layer separation. In particular, we propose to use two descriptors in hyperspectral historical document analysis, namely hyper-hue and extended multi-attribute profile (EMAP). Our comparative results with other features underline the efficacy of the three proposed improvements.
Hyper-Hue and EMAP on Hyperspectral Images for Supervised Layer Decomposition of Old Master Drawings
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http://arxiv.org/pdf/1801.09472v1
Title HyperHue EMAP Hyperspectral Images Supervised Layer Decomposition Old Master Drawings Summary Old master drawing mostly created step step several layer using different material art historian restorer examination layer brings various insight artistic work process help answer question object attribution authenticity However layer typically overlap oftentimes difficult differentiate unaided eye example common layer combination red chalk ink work propose image processing pipeline operates hyperspectral image separate layer Using pipeline show hyperspectral image enable better layer separation RGB image spectral focus stacking aid layer separation particular propose use two descriptor hyperspectral historical document analysis namely hyperhue extended multiattribute profile EMAP comparative result feature underline efficacy three proposed improvement Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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['Suman Kunwar', 'Manchana Shrestha', 'Rojesh Man Shikhrakar']
1801.10031v1
Malaria is mosquito-borne blood disease caused by parasites of the genus Plasmodium. Conventional diagnostic tool for malaria is the examination of stained blood cell of patient in microscope. The blood to be tested is placed in a slide and is observed under a microscope to count the number of infected RBC. An expert technician is involved in the examination of the slide with intense visual and mental concentration. This is tiresome and time consuming process. In this paper, we construct a new mage processing system for detection and quantification of plasmodium parasites in blood smear slide, later we develop Machine Learning algorithm to learn, detect and determine the types of infected cells according to its features.
Malaria Detection Using Image Processing and Machine Learning
2,018
http://arxiv.org/pdf/1801.10031v1
Title Malaria Detection Using Image Processing Machine Learning Summary Malaria mosquitoborne blood disease caused parasite genus Plasmodium Conventional diagnostic tool malaria examination stained blood cell patient microscope blood tested placed slide observed microscope count number infected RBC expert technician involved examination slide intense visual mental concentration tiresome time consuming process paper construct new mage processing system detection quantification plasmodium parasite blood smear slide later develop Machine Learning algorithm learn detect determine type infected cell according feature Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,217
40,217
['Hai-Chao Shi', 'Xiao-Yu Zhang']
1801.10365v1
As a means for secret communication, steganography aims at concealing a message within a medium such that the presence of the hidden message can hardly be detected. In computer vision tasks, adversarial training has be-come a competitive learning method to generate images. However, the gen-erative tasks are confronted with great challenge on synthesizing images. This paper studies the mechanism of applying adversarial learning for dis-criminative tasks to learn the steganographic algorithm. We show that through unsupervised adversarial training, the adversarial model can pro-duce robust steganographic solutions, which act like an encryption. Through supervised adversarial training, we can also train a robust ste-ganalyzer, which is utilized to discriminate whether an image contains se-cret information. Our model is composed of three modules, i.e. a generator, a discriminator and a steganalyzer. All the three members are trained simulta-neously. To formulate the algorithm, we use a game to represent the com-munication between the three parties. In this game, the generator and dis-criminator attempt to communicate with each other with secret messages hidden in an image. While the steganalyzer attempts to analyze whether there is a transmission of confidential information. Experimental results demonstrate the effectiveness of the proposed method on steganography tasks.
Synchronization Detection and Recovery of Steganographic Messages with Adversarial Learning
2,018
http://arxiv.org/pdf/1801.10365v1
Title Synchronization Detection Recovery Steganographic Messages Adversarial Learning Summary mean secret communication steganography aim concealing message within medium presence hidden message hardly detected computer vision task adversarial training become competitive learning method generate image However generative task confronted great challenge synthesizing image paper study mechanism applying adversarial learning discriminative task learn steganographic algorithm show unsupervised adversarial training adversarial model produce robust steganographic solution act like encryption supervised adversarial training also train robust steganalyzer utilized discriminate whether image contains secret information model composed three module ie generator discriminator steganalyzer three member trained simultaneously formulate algorithm use game represent communication three party game generator discriminator attempt communicate secret message hidden image steganalyzer attempt analyze whether transmission confidential information Experimental result demonstrate effectiveness proposed method steganography task Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,218
40,218
['Zhong Li', 'Yu Ji', 'Wei Yang', 'Jinwei Ye', 'Jingyi Yu']
1801.10434v1
In multi-view human body capture systems, the recovered 3D geometry or even the acquired imagery data can be heavily corrupted due to occlusions, noise, limited field of- view, etc. Direct estimation of 3D pose, body shape or motion on these low-quality data has been traditionally challenging.In this paper, we present a graph-based non-rigid shape registration framework that can simultaneously recover 3D human body geometry and estimate pose/motion at high fidelity.Our approach first generates a global full-body template by registering all poses in the acquired motion sequence.We then construct a deformable graph by utilizing the rigid components in the global template. We directly warp the global template graph back to each motion frame in order to fill in missing geometry. Specifically, we combine local rigidity and temporal coherence constraints to maintain geometry and motion consistencies. Comprehensive experiments on various scenes show that our method is accurate and robust even in the presence of drastic motions.
Robust 3D Human Motion Reconstruction Via Dynamic Template Construction
2,018
http://arxiv.org/pdf/1801.10434v1
Title Robust 3D Human Motion Reconstruction Via Dynamic Template Construction Summary multiview human body capture system recovered 3D geometry even acquired imagery data heavily corrupted due occlusion noise limited field view etc Direct estimation 3D pose body shape motion lowquality data traditionally challengingIn paper present graphbased nonrigid shape registration framework simultaneously recover 3D human body geometry estimate posemotion high fidelityOur approach first generates global fullbody template registering pose acquired motion sequenceWe construct deformable graph utilizing rigid component global template directly warp global template graph back motion frame order fill missing geometry Specifically combine local rigidity temporal coherence constraint maintain geometry motion consistency Comprehensive experiment various scene show method accurate robust even presence drastic motion Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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['Haohan Li', 'Zuoqiang Shi', 'Xiaoping Wang']
1801.10441v1
In this paper, a novel weighted nonlocal total variation (WNTV) method is proposed. Compared to the classical nonlocal total variation methods, our method modifies the energy functional to introduce a weight to balance between the labeled sets and unlabeled sets. With extensive numerical examples in semi-supervised clustering, image inpainting and image colorization, we demonstrate that WNTV provides an effective and efficient method in many image processing and machine learning problems.
Weighted Nonlocal Total Variation in Image Processing
2,018
http://arxiv.org/pdf/1801.10441v1
Title Weighted Nonlocal Total Variation Image Processing Summary paper novel weighted nonlocal total variation WNTV method proposed Compared classical nonlocal total variation method method modifies energy functional introduce weight balance labeled set unlabeled set extensive numerical example semisupervised clustering image inpainting image colorization demonstrate WNTV provides effective efficient method many image processing machine learning problem Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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['Filip Malmberg']
1802.00624v1
Techniques based on minimal graph cuts have become a standard tool for solving combinatorial optimization problems arising in image processing and computer vision applications. These techniques can be used to minimize objective functions written as the sum of a set of unary and pairwise terms, provided that the objective function is submodular. This can be interpreted as minimizing the $l_1$-norm of the vector containing all pairwise and unary terms. By raising each term to a power $p$, the same technique can also be used to minimize the $l_p$-norm of the vector. Unfortunately, the submodularity of an $l_1$-norm objective function does not guarantee the submodularity of the corresponding $l_p$-norm objective function. The contribution of this paper is to provide useful conditions under which an $l_p$-norm objective function is submodular for all $p\geq 1$, thereby identifying a large class of $l_p$-norm objective functions that can be minimized via minimal graph cuts.
When can $l_p$-norm objective functions be minimized via graph cuts?
2,018
http://arxiv.org/pdf/1802.00624v1
Title lpnorm objective function minimized via graph cut Summary Techniques based minimal graph cut become standard tool solving combinatorial optimization problem arising image processing computer vision application technique used minimize objective function written sum set unary pairwise term provided objective function submodular interpreted minimizing l1norm vector containing pairwise unary term raising term power p technique also used minimize lpnorm vector Unfortunately submodularity l1norm objective function guarantee submodularity corresponding lpnorm objective function contribution paper provide useful condition lpnorm objective function submodular pgeq 1 thereby identifying large class lpnorm objective function minimized via minimal graph cut Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,221
40,221
['Tomoyoshi Shimobaba', 'Takashi Kakue', 'Tomoyoshi Ito']
1802.00664v1
Digital holography enables us to reconstruct objects in three-dimensional space from holograms captured by an imaging device. For the reconstruction, we need to know the depth position of the recoded object in advance. In this study, we propose depth prediction using convolutional neural network (CNN)-based regression. In the previous researches, the depth of an object was estimated through reconstructed images at different depth positions from a hologram using a certain metric that indicates the most focused depth position; however, such a depth search is time-consuming. The CNN of the proposed method can directly predict the depth position with millimeter precision from holograms.
Convolutional neural network-based regression for depth prediction in digital holography
2,018
http://arxiv.org/pdf/1802.00664v1
Title Convolutional neural networkbased regression depth prediction digital holography Summary Digital holography enables u reconstruct object threedimensional space hologram captured imaging device reconstruction need know depth position recoded object advance study propose depth prediction using convolutional neural network CNNbased regression previous research depth object estimated reconstructed image different depth position hologram using certain metric indicates focused depth position however depth search timeconsuming CNN proposed method directly predict depth position millimeter precision hologram Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,222
['Zhun Fan', 'Zhongxing Li', 'Wenji Li']
1802.01116v1
Object recognition and sorting plays a key role in robotic systems, especially for the autonomous robots to implement object sorting tasks in a warehouse. In this paper, we present a global texture-shape 3D feature descriptor which can be utilized in a sorting system, and this system can perform object sorting tasks well. Our proposed descriptor stems from the clustered viewpoint feature histogram (CVFH). As the CVFH feature descriptor relies on the geometrical information of the whole 3D object surface only, it can not perform well on the objects with similar geometrical information. Therefore, we extend the CVFH descriptor with texture information to generate a new global 3D feature descriptor. Then this proposed descriptor is tested for sorting 3D objects by using multi-class support vector machines (SVM). It is also evaluated by a public 3D image dataset and real scenes. The results of evaluation show that our proposed descriptor have a good performance for object recognition compared to the CVFH. Then we leverage this proposed descriptor in the proposed sorting system, showing that the proposed descriptor helps the sorting system implement the object detection, the object recognition and object grasping tasks well.
Object Detection and Sorting by Using a Global Texture-Shape 3D Feature Descriptor
2,018
http://arxiv.org/pdf/1802.01116v1
Title Object Detection Sorting Using Global TextureShape 3D Feature Descriptor Summary Object recognition sorting play key role robotic system especially autonomous robot implement object sorting task warehouse paper present global textureshape 3D feature descriptor utilized sorting system system perform object sorting task well proposed descriptor stem clustered viewpoint feature histogram CVFH CVFH feature descriptor relies geometrical information whole 3D object surface perform well object similar geometrical information Therefore extend CVFH descriptor texture information generate new global 3D feature descriptor proposed descriptor tested sorting 3D object using multiclass support vector machine SVM also evaluated public 3D image dataset real scene result evaluation show proposed descriptor good performance object recognition compared CVFH leverage proposed descriptor proposed sorting system showing proposed descriptor help sorting system implement object detection object recognition object grasping task well Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,223
40,223
['Kévin Degraux', 'Valerio Cambareri', 'Bert Geelen', 'Laurent Jacques', 'Gauthier Lafruit']
1802.02040v1
This paper introduces two acquisition device architectures for multispectral compressive imaging. Unlike most existing methods, the proposed computational imaging techniques do not include any dispersive element, as they use a dedicated sensor which integrates narrowband Fabry-P\'erot spectral filters at the pixel level. The first scheme leverages joint inpainting and super-resolution to fill in those voxels that are missing due to the device's limited pixel count. The second scheme, in link with compressed sensing, introduces spatial random convolutions, but is more complex and may be affected by diffraction. In both cases we solve the associated inverse problems by using the same signal prior. Specifically, we propose a redundant analysis signal prior in a convex formulation. Through numerical simulations, we explore different realistic setups. Our objective is also to highlight some practical guidelines and discuss their complexity trade-offs to integrate these schemes into actual computational imaging systems. Our conclusion is that the second technique performs best at high compression levels, in a properly sized and calibrated setup. Otherwise, the first, simpler technique should be favored.
Multispectral Compressive Imaging Strategies using Fabry-Pérot Filtered Sensors
2,018
http://arxiv.org/pdf/1802.02040v1
Title Multispectral Compressive Imaging Strategies using FabryPérot Filtered Sensors Summary paper introduces two acquisition device architecture multispectral compressive imaging Unlike existing method proposed computational imaging technique include dispersive element use dedicated sensor integrates narrowband FabryPerot spectral filter pixel level first scheme leverage joint inpainting superresolution fill voxels missing due device limited pixel count second scheme link compressed sensing introduces spatial random convolution complex may affected diffraction case solve associated inverse problem using signal prior Specifically propose redundant analysis signal prior convex formulation numerical simulation explore different realistic setup objective also highlight practical guideline discus complexity tradeoff integrate scheme actual computational imaging system conclusion second technique performs best high compression level properly sized calibrated setup Otherwise first simpler technique favored Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,224
['Ramyad Hadidi', 'Jiashen Cao', 'Matthew Woodward', 'Michael S. Ryoo', 'Hyesoon Kim']
1802.02138v3
The prevalence of Internet of things (IoT) devices and abundance of sensor data has created an increase in real-time data processing such as recognition of speech, image, and video. While currently such processes are offloaded to the computationally powerful cloud system, a localized and distributed approach is desirable because (i) it preserves the privacy of users and (ii) it omits the dependency on cloud services. However, IoT networks are usually composed of resource-constrained devices, and a single device is not powerful enough to process real-time data. To overcome this challenge, we examine data and model parallelism for such devices in the context of deep neural networks. We propose Musical Chair to enable efficient, localized, and dynamic real-time recognition by harvesting the aggregated computational power from the resource-constrained devices in the same IoT network as input sensors. Musical chair adapts to the availability of computing devices at runtime and adjusts to the inherit dynamics of IoT networks. To demonstrate Musical Chair, on a network of Raspberry PIs (up to 12) each connected to a camera, we implement a state-of-the-art action recognition model for videos and two recognition models for images. Compared to the Tegra TX2, an embedded low-power platform with a six-core CPU and a GPU, our distributed action recognition system achieves not only similar energy consumption but also twice the performance of the TX2. Furthermore, in image recognition, Musical Chair achieves similar performance and saves dynamic energy.
Musical Chair: Efficient Real-Time Recognition Using Collaborative IoT Devices
2,018
http://arxiv.org/pdf/1802.02138v3
Title Musical Chair Efficient RealTime Recognition Using Collaborative IoT Devices Summary prevalence Internet thing IoT device abundance sensor data created increase realtime data processing recognition speech image video currently process offloaded computationally powerful cloud system localized distributed approach desirable preserve privacy user ii omits dependency cloud service However IoT network usually composed resourceconstrained device single device powerful enough process realtime data overcome challenge examine data model parallelism device context deep neural network propose Musical Chair enable efficient localized dynamic realtime recognition harvesting aggregated computational power resourceconstrained device IoT network input sensor Musical chair adapts availability computing device runtime adjusts inherit dynamic IoT network demonstrate Musical Chair network Raspberry PIs 12 connected camera implement stateoftheart action recognition model video two recognition model image Compared Tegra TX2 embedded lowpower platform sixcore CPU GPU distributed action recognition system achieves similar energy consumption also twice performance TX2 Furthermore image recognition Musical Chair achieves similar performance save dynamic energy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,225
40,225
['Stefan Hoermann', 'Philipp Henzler', 'Martin Bach', 'Klaus Dietmayer']
1802.02202v1
We tackle the problem of object detection and pose estimation in a shared space downtown environment. For perception multiple laser scanners with 360{\deg} coverage were fused in a dynamic occupancy grid map (DOGMa). A single-stage deep convolutional neural network is trained to provide object hypotheses comprising of shape, position, orientation and an existence score from a single input DOGMa. Furthermore, an algorithm for offline object extraction was developed to automatically label several hours of training data. The algorithm is based on a two-pass trajectory extraction, forward and backward in time. Typical for engineered algorithms, the automatic label generation suffers from misdetections, which makes hard negative mining impractical. Therefore, we propose a loss function counteracting the high imbalance between mostly static background and extremely rare dynamic grid cells. Experiments indicate, that the trained network has good generalization capabilities since it detects objects occasionally lost by the label algorithm. Evaluation reaches an average precision (AP) of 75.9%
Object Detection on Dynamic Occupancy Grid Maps Using Deep Learning and Automatic Label Generation
2,018
http://arxiv.org/pdf/1802.02202v1
Title Object Detection Dynamic Occupancy Grid Maps Using Deep Learning Automatic Label Generation Summary tackle problem object detection pose estimation shared space downtown environment perception multiple laser scanner 360deg coverage fused dynamic occupancy grid map DOGMa singlestage deep convolutional neural network trained provide object hypothesis comprising shape position orientation existence score single input DOGMa Furthermore algorithm offline object extraction developed automatically label several hour training data algorithm based twopass trajectory extraction forward backward time Typical engineered algorithm automatic label generation suffers misdetections make hard negative mining impractical Therefore propose loss function counteracting high imbalance mostly static background extremely rare dynamic grid cell Experiments indicate trained network good generalization capability since detects object occasionally lost label algorithm Evaluation reach average precision AP 759 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,226
40,226
['Jonito Aerts Arguelles']
1802.02216v1
We analyse the way in which the principle that 'the whole is greater than the sum of its parts' manifests itself with phenomena of visual perception. For this investigation we use insights and techniques coming from quantum cognition, and more specifically we are inspired by the correspondence of this principle with the phenomenon of the conjunction effect in human cognition. We identify entities of meaning within artefacts of visual perception and rely on how such entities are modelled for corpuses of texts such as the webpages of the World-Wide Web for our study of how they appear in phenomena of visual perception. We identify concretely the conjunction effect in visual artefacts and analyse its structure in the example of a photograph. We also analyse quantum entanglement between different aspects of meaning in artefacts of visual perception. We confirm its presence by showing that well elected experiments on images retrieved accordingly by Google Images give rise to probabilities and expectation values violating the Clauser Horne Shimony Holt version of Bell's inequalities. We point out how this approach can lead to a mathematical description of the meaning content of a visual artefact such as a photograph.
The Heart of an Image: Quantum Superposition and Entanglement in Visual Perception
2,018
http://arxiv.org/pdf/1802.02216v1
Title Heart Image Quantum Superposition Entanglement Visual Perception Summary analyse way principle whole greater sum part manifest phenomenon visual perception investigation use insight technique coming quantum cognition specifically inspired correspondence principle phenomenon conjunction effect human cognition identify entity meaning within artefact visual perception rely entity modelled corpus text webpage WorldWide Web study appear phenomenon visual perception identify concretely conjunction effect visual artefact analyse structure example photograph also analyse quantum entanglement different aspect meaning artefact visual perception confirm presence showing well elected experiment image retrieved accordingly Google Images give rise probability expectation value violating Clauser Horne Shimony Holt version Bells inequality point approach lead mathematical description meaning content visual artefact photograph Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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['Ethan C. Jackson', 'James Alexander Hughes', 'Mark Daley']
1802.02423v1
In contrast to conventional, univariate analysis, various types of multivariate analysis have been applied to functional magnetic resonance imaging (fMRI) data. In this paper, we compare two contemporary approaches for multivariate regression on task-based fMRI data: linear regression with ridge regularization and non-linear symbolic regression using genetic programming. The data for this project is representative of a contemporary fMRI experimental design for visual stimuli. Linear and non-linear models were generated for 10 subjects, with another 4 withheld for validation. Model quality is evaluated by comparing $R$ scores (Pearson product-moment correlation) in various contexts, including single run self-fit, within-subject generalization, and between-subject generalization. Propensity for modelling strategies to overfit is estimated using a separate resting state scan. Results suggest that neither method is objectively or inherently better than the other.
On the Generalizability of Linear and Non-Linear Region of Interest-Based Multivariate Regression Models for fMRI Data
2,018
http://arxiv.org/pdf/1802.02423v1
Title Generalizability Linear NonLinear Region InterestBased Multivariate Regression Models fMRI Data Summary contrast conventional univariate analysis various type multivariate analysis applied functional magnetic resonance imaging fMRI data paper compare two contemporary approach multivariate regression taskbased fMRI data linear regression ridge regularization nonlinear symbolic regression using genetic programming data project representative contemporary fMRI experimental design visual stimulus Linear nonlinear model generated 10 subject another 4 withheld validation Model quality evaluated comparing R score Pearson productmoment correlation various context including single run selffit withinsubject generalization betweensubject generalization Propensity modelling strategy overfit estimated using separate resting state scan Results suggest neither method objectively inherently better Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,228
40,228
['Lele Chen', 'Yue Wu', 'Adora M. DSouza', 'Anas Z. Abidin', 'Axel Wismuller', 'Chenliang Xu']
1802.02427v2
Glioma is one of the most common and aggressive types of primary brain tumors. The accurate segmentation of subcortical brain structures is crucial to the study of gliomas in that it helps the monitoring of the progression of gliomas and aids the evaluation of treatment outcomes. However, the large amount of required human labor makes it difficult to obtain the manually segmented Magnetic Resonance Imaging (MRI) data, limiting the use of precise quantitative measurements in the clinical practice. In this work, we try to address this problem by developing a 3D Convolutional Neural Network~(3D CNN) based model to automatically segment gliomas. The major difficulty of our segmentation model comes with the fact that the location, structure, and shape of gliomas vary significantly among different patients. In order to accurately classify each voxel, our model captures multi-scale contextual information by extracting features from two scales of receptive fields. To fully exploit the tumor structure, we propose a novel architecture that hierarchically segments different lesion regions of the necrotic and non-enhancing tumor~(NCR/NET), peritumoral edema~(ED) and GD-enhancing tumor~(ET). Additionally, we utilize densely connected convolutional blocks to further boost the performance. We train our model with a patch-wise training schema to mitigate the class imbalance problem. The proposed method is validated on the BraTS 2017 dataset and it achieves Dice scores of 0.72, 0.83 and 0.81 for the complete tumor, tumor core and enhancing tumor, respectively. These results are comparable to the reported state-of-the-art results, and our method is better than existing 3D-based methods in terms of compactness, time and space efficiency.
MRI Tumor Segmentation with Densely Connected 3D CNN
2,018
http://arxiv.org/pdf/1802.02427v2
Title MRI Tumor Segmentation Densely Connected 3D CNN Summary Glioma one common aggressive type primary brain tumor accurate segmentation subcortical brain structure crucial study glioma help monitoring progression glioma aid evaluation treatment outcome However large amount required human labor make difficult obtain manually segmented Magnetic Resonance Imaging MRI data limiting use precise quantitative measurement clinical practice work try address problem developing 3D Convolutional Neural Network3D CNN based model automatically segment glioma major difficulty segmentation model come fact location structure shape glioma vary significantly among different patient order accurately classify voxel model capture multiscale contextual information extracting feature two scale receptive field fully exploit tumor structure propose novel architecture hierarchically segment different lesion region necrotic nonenhancing tumorNCRNET peritumoral edemaED GDenhancing tumorET Additionally utilize densely connected convolutional block boost performance train model patchwise training schema mitigate class imbalance problem proposed method validated BraTS 2017 dataset achieves Dice score 072 083 081 complete tumor tumor core enhancing tumor respectively result comparable reported stateoftheart result method better existing 3Dbased method term compactness time space efficiency Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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['Bing Zhang', 'Chengming Xu', 'Chang Mao Cheng', 'Yanwei Fu', 'Yu-Gang Jiang', 'Xiangyang Xue']
1802.02774v1
This paper focuses on fully understanding the figure skating sport videos. In particular, we present a large-scale figure skating sport video dataset, which include 500 figure skating videos. On average, the length of each video is 2 minute and 50 seconds. Each video is annotated by three scores from nine different referees, i.e., Total Element Score(TES), Total Program Component Score (PCS), and Total Deductions(DED). The players of this dataset come from more than 20 different countries. We compare different features and models to predict the scores of each video. We also derive a video summarization dataset of 476 videos with the ground-truth video summary produced from the great shot. A reinforcement learning based video summarization algorithm is proposed here; and the experiments show better performance than the other baseline video summarization algorithms.
Learning to score and summarize figure skating sport videos
2,018
http://arxiv.org/pdf/1802.02774v1
Title Learning score summarize figure skating sport video Summary paper focus fully understanding figure skating sport video particular present largescale figure skating sport video dataset include 500 figure skating video average length video 2 minute 50 second video annotated three score nine different referee ie Total Element ScoreTES Total Program Component Score PCS Total DeductionsDED player dataset come 20 different country compare different feature model predict score video also derive video summarization dataset 476 video groundtruth video summary produced great shot reinforcement learning based video summarization algorithm proposed experiment show better performance baseline video summarization algorithm Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,230
['Timur Kuzhagaliyev', 'Neil T. Clancy', 'Mirek Janatka', 'Kevin Tchaka', 'Francisco Vasconcelos', 'Matthew J. Clarkson', 'Kurinchi Gurusamy', 'David J. Hawkes', 'Brian Davidson', 'Danail Stoyanov']
1802.03274v1
Irreversible electroporation (IRE) is a soft tissue ablation technique suitable for treatment of inoperable tumours in the pancreas. The process involves applying a high voltage electric field to the tissue containing the mass using needle electrodes, leaving cancerous cells irreversibly damaged and vulnerable to apoptosis. Efficacy of the treatment depends heavily on the accuracy of needle placement and requires a high degree of skill from the operator. In this paper, we describe an Augmented Reality (AR) system designed to overcome the challenges associated with planning and guiding the needle insertion process. Our solution, based on the HoloLens (Microsoft, USA) platform, tracks the position of the headset, needle electrodes and ultrasound (US) probe in space. The proof of concept implementation of the system uses this tracking data to render real-time holographic guides on the HoloLens, giving the user insight into the current progress of needle insertion and an indication of the target needle trajectory. The operator's field of view is augmented using visual guides and real-time US feed rendered on a holographic plane, eliminating the need to consult external monitors. Based on these early prototypes, we are aiming to develop a system that will lower the skill level required for IRE while increasing overall accuracy of needle insertion and, hence, the likelihood of successful treatment.
Augmented Reality needle ablation guidance tool for Irreversible Electroporation in the pancreas
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http://arxiv.org/pdf/1802.03274v1
Title Augmented Reality needle ablation guidance tool Irreversible Electroporation pancreas Summary Irreversible electroporation IRE soft tissue ablation technique suitable treatment inoperable tumour pancreas process involves applying high voltage electric field tissue containing mass using needle electrode leaving cancerous cell irreversibly damaged vulnerable apoptosis Efficacy treatment depends heavily accuracy needle placement requires high degree skill operator paper describe Augmented Reality AR system designed overcome challenge associated planning guiding needle insertion process solution based HoloLens Microsoft USA platform track position headset needle electrode ultrasound US probe space proof concept implementation system us tracking data render realtime holographic guide HoloLens giving user insight current progress needle insertion indication target needle trajectory operator field view augmented using visual guide realtime US feed rendered holographic plane eliminating need consult external monitor Based early prototype aiming develop system lower skill level required IRE increasing overall accuracy needle insertion hence likelihood successful treatment Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,231
40,231
['Tingran Gao', 'Shahar Z. Kovalsky', 'Doug M. Boyer', 'Ingrid Daubechies']
1802.03479v1
As a means of improving analysis of biological shapes, we propose a greedy algorithm for sampling a Riemannian manifold based on the uncertainty of a Gaussian process. This is known to produce a near optimal experimental design with the manifold as the domain, and appears to outperform the use of user-placed landmarks in representing geometry of biological objects. We provide an asymptotic analysis for the decay of the maximum conditional variance, which is frequently employed as a greedy criterion for similar variance- or uncertainty-based sequential experimental design strategies, to our knowledge this is the first result of this type for experimental design. The key observation is to link the greedy algorithm with reduced basis methods in the context of model reduction for partial differential equations. We apply the proposed landmarking algorithm to geometric morphometrics, a branch of evolutionary biology focusing on the analysis and comparisons of anatomical shapes, and compare the automatically sampled landmarks with the "ground truth" landmarks manually placed by evolutionary anthropologists, the results suggest that Gaussian process landmarks perform equally well or better, in terms of both spatial coverage and downstream statistical analysis. We expect this approach will find additional applications in other fields of research.
Gaussian Process Landmarking on Manifolds
2,018
http://arxiv.org/pdf/1802.03479v1
Title Gaussian Process Landmarking Manifolds Summary mean improving analysis biological shape propose greedy algorithm sampling Riemannian manifold based uncertainty Gaussian process known produce near optimal experimental design manifold domain appears outperform use userplaced landmark representing geometry biological object provide asymptotic analysis decay maximum conditional variance frequently employed greedy criterion similar variance uncertaintybased sequential experimental design strategy knowledge first result type experimental design key observation link greedy algorithm reduced basis method context model reduction partial differential equation apply proposed landmarking algorithm geometric morphometrics branch evolutionary biology focusing analysis comparison anatomical shape compare automatically sampled landmark ground truth landmark manually placed evolutionary anthropologist result suggest Gaussian process landmark perform equally well better term spatial coverage downstream statistical analysis expect approach find additional application field research Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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['Wenhao Ding', 'Shuaijun Li', 'Guilin Zhang', 'Xiangyu Lei', 'Huihuan Qian', 'Yangsheng Xu']
1802.03515v3
In this paper, we present an accurate approach to estimate vehicles' pose and shape from off-board multiview images. The images are taken by monocular cameras and have small overlaps. We utilize state-of-the-art convolutional neural networks (CNNs) to extract vehicles' semantic keypoints and introduce a Cross Projection Optimization (CPO) method to estimate the 3D pose. During the iterative CPO process, an adaptive shape adjustment method named Hierarchical Wireframe Constraint (HWC) is implemented to estimate the shape. Our approach is evaluated under both simulated and real-world scenes for performance verification. It's shown that our algorithm outperforms other existing monocular and stereo methods for vehicles' pose and shape estimation. This approach provides a new and robust solution for off-board visual vehicle localization and tracking, which can be applied to massive surveillance camera networks for intelligent transportation.
Vehicle Pose and Shape Estimation through Multiple Monocular Vision
2,018
http://arxiv.org/pdf/1802.03515v3
Title Vehicle Pose Shape Estimation Multiple Monocular Vision Summary paper present accurate approach estimate vehicle pose shape offboard multiview image image taken monocular camera small overlap utilize stateoftheart convolutional neural network CNNs extract vehicle semantic keypoints introduce Cross Projection Optimization CPO method estimate 3D pose iterative CPO process adaptive shape adjustment method named Hierarchical Wireframe Constraint HWC implemented estimate shape approach evaluated simulated realworld scene performance verification shown algorithm outperforms existing monocular stereo method vehicle pose shape estimation approach provides new robust solution offboard visual vehicle localization tracking applied massive surveillance camera network intelligent transportation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,233
40,233
['Jiawei Su', 'Danilo Vasconcellos Vargas', 'Sanjiva Prasad', 'Daniele Sgandurra', 'Yaokai Feng', 'Kouichi Sakurai']
1802.03714v1
The Internet of Things (IoT) is an extension of the traditional Internet, which allows a very large number of smart devices, such as home appliances, network cameras, sensors and controllers to connect to one another to share information and improve user experiences. Current IoT devices are typically micro-computers for domain-specific computations rather than traditional functionspecific embedded devices. Therefore, many existing attacks, targeted at traditional computers connected to the Internet, may also be directed at IoT devices. For example, DDoS attacks have become very common in IoT environments, as these environments currently lack basic security monitoring and protection mechanisms, as shown by the recent Mirai and Brickerbot IoT botnets. In this paper, we propose a novel light-weight approach for detecting DDos malware in IoT environments.We firstly extract one-channel gray-scale images converted from binaries, and then utilize a lightweight convolutional neural network for classifying IoT malware families. The experimental results show that the proposed system can achieve 94.0% accuracy for the classification of goodware and DDoS malware, and 81.8% accuracy for the classification of goodware and two main malware families.
Lightweight Classification of IoT Malware based on Image Recognition
2,018
http://arxiv.org/pdf/1802.03714v1
Title Lightweight Classification IoT Malware based Image Recognition Summary Internet Things IoT extension traditional Internet allows large number smart device home appliance network camera sensor controller connect one another share information improve user experience Current IoT device typically microcomputer domainspecific computation rather traditional functionspecific embedded device Therefore many existing attack targeted traditional computer connected Internet may also directed IoT device example DDoS attack become common IoT environment environment currently lack basic security monitoring protection mechanism shown recent Mirai Brickerbot IoT botnets paper propose novel lightweight approach detecting DDos malware IoT environmentsWe firstly extract onechannel grayscale image converted binary utilize lightweight convolutional neural network classifying IoT malware family experimental result show proposed system achieve 940 accuracy classification goodware DDoS malware 818 accuracy classification goodware two main malware family Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,234
40,234
['Mina Khoshdeli', 'Bahram Parvin']
1802.04427v2
Nuclear segmentation is an important step for profiling aberrant regions of histology sections. However, segmentation is a complex problem as a result of variations in nuclear geometry (e.g., size, shape), nuclear type (e.g., epithelial, fibroblast), and nuclear phenotypes (e.g., vesicular, aneuploidy). The problem is further complicated as a result of variations in sample preparation. It is shown and validated that fusion of very deep convolutional networks overcomes (i) complexities associated with multiple nuclear phenotypes, and (ii) separation of overlapping nuclei. The fusion relies on integrating of networks that learn region- and boundary-based representations. The system has been validated on a diverse set of nuclear phenotypes that correspond to the breast and brain histology sections.
Deep Learning Models Delineates Multiple Nuclear Phenotypes in H&E Stained Histology Sections
2,018
http://arxiv.org/pdf/1802.04427v2
Title Deep Learning Models Delineates Multiple Nuclear Phenotypes Stained Histology Sections Summary Nuclear segmentation important step profiling aberrant region histology section However segmentation complex problem result variation nuclear geometry eg size shape nuclear type eg epithelial fibroblast nuclear phenotype eg vesicular aneuploidy problem complicated result variation sample preparation shown validated fusion deep convolutional network overcomes complexity associated multiple nuclear phenotype ii separation overlapping nucleus fusion relies integrating network learn region boundarybased representation system validated diverse set nuclear phenotype correspond breast brain histology section Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,235
40,235
['Chawin Sitawarin', 'Arjun Nitin Bhagoji', 'Arsalan Mosenia', 'Mung Chiang', 'Prateek Mittal']
1802.06430v2
Sign recognition is an integral part of autonomous cars. Any misclassification of traffic signs can potentially lead to a multitude of disastrous consequences, ranging from a life-threatening accident to a large-scale interruption of transportation services relying on autonomous cars. In this paper, we propose and examine realistic security attacks against sign recognition systems for Deceiving Autonomous caRs with Toxic Signs (we call the proposed attacks DARTS). Leveraging the concept of adversarial examples, we modify innocuous signs/advertisements in the environment in such a way that they seem normal to human observers but are interpreted as the adversary's desired traffic sign by autonomous cars. Further, we pursue a fundamentally different perspective to attacking autonomous cars, motivated by the observation that the driver and vehicle-mounted camera see the environment from different angles (the camera commonly sees the road with a higher angle, e.g., from top of the car). We propose a novel attack against vehicular sign recognition systems: we create signs that change as they are viewed from different angles, and thus, can be interpreted differently by the driver and sign recognition. We extensively evaluate the proposed attacks under various conditions: different distances, lighting conditions, and camera angles. We first examine our attacks virtually, i.e., we check if the digital images of toxic signs can deceive the sign recognition system. Further, we investigate the effectiveness of attacks in real-world settings: we print toxic signs, install them in the environment, capture videos using a vehicle-mounted camera, and process them using our sign recognition pipeline.
DARTS: Deceiving Autonomous Cars with Toxic Signs
2,018
http://arxiv.org/pdf/1802.06430v2
Title DARTS Deceiving Autonomous Cars Toxic Signs Summary Sign recognition integral part autonomous car misclassification traffic sign potentially lead multitude disastrous consequence ranging lifethreatening accident largescale interruption transportation service relying autonomous car paper propose examine realistic security attack sign recognition system Deceiving Autonomous caRs Toxic Signs call proposed attack DARTS Leveraging concept adversarial example modify innocuous signsadvertisements environment way seem normal human observer interpreted adversary desired traffic sign autonomous car pursue fundamentally different perspective attacking autonomous car motivated observation driver vehiclemounted camera see environment different angle camera commonly see road higher angle eg top car propose novel attack vehicular sign recognition system create sign change viewed different angle thus interpreted differently driver sign recognition extensively evaluate proposed attack various condition different distance lighting condition camera angle first examine attack virtually ie check digital image toxic sign deceive sign recognition system investigate effectiveness attack realworld setting print toxic sign install environment capture video using vehiclemounted camera process using sign recognition pipeline Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,236
40,236
['Tat-Jun Chin', 'Zhipeng Cai', 'Frank Neumann']
1802.06464v2
Robust model fitting plays a vital role in computer vision, and research into algorithms for robust fitting continues to be active. Arguably the most popular paradigm for robust fitting in computer vision is consensus maximisation, which strives to find the model parameters that maximise the number of inliers. Despite the significant developments in algorithms for consensus maximisation, there has been a lack of fundamental analysis of the problem in the computer vision literature. In particular, whether consensus maximisation is "tractable" remains a question that has not been rigorously dealt with, thus making it difficult to assess and compare the performance of proposed algorithms, relative to what is theoretically achievable. To shed light on these issues, we present several computational hardness results for consensus maximisation. Our results underline the fundamental intractability of the problem, and resolve several ambiguities existing in the literature.
Robust Fitting in Computer Vision: Easy or Hard?
2,018
http://arxiv.org/pdf/1802.06464v2
Title Robust Fitting Computer Vision Easy Hard Summary Robust model fitting play vital role computer vision research algorithm robust fitting continues active Arguably popular paradigm robust fitting computer vision consensus maximisation strives find model parameter maximise number inliers Despite significant development algorithm consensus maximisation lack fundamental analysis problem computer vision literature particular whether consensus maximisation tractable remains question rigorously dealt thus making difficult ass compare performance proposed algorithm relative theoretically achievable shed light issue present several computational hardness result consensus maximisation result underline fundamental intractability problem resolve several ambiguity existing literature Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,237
40,237
['Timur M. Khanipov']
1802.06619v1
It is demonstrated that the classical Hough transform with shift-elevation parametrization of digital straight lines has additive complexity of at most $\mathcal{O}(n^3 / \log n)$ on a $n\times n$ image. The proof is constructive and uses ensemble computation approach to build summation circuits. The proposed method has similarities with the fast Hough transform (FHT) and may be considered a form of the "divide and conquer" technique. It is based on the fact that lines with close slopes can be decomposed into common components, allowing generalization for other pattern families. When applied to FHT patterns, the algorithm yields exactly the $\Theta(n^2\log n)$ FHT asymptotics which might suggest that the actual classical Hough transform circuits could smaller size than $\Theta(n^3/ \log n)$.
Ensemble computation approach to the Hough transform
2,018
http://arxiv.org/pdf/1802.06619v1
Title Ensemble computation approach Hough transform Summary demonstrated classical Hough transform shiftelevation parametrization digital straight line additive complexity mathcalOn3 log n ntimes n image proof constructive us ensemble computation approach build summation circuit proposed method similarity fast Hough transform FHT may considered form divide conquer technique based fact line close slope decomposed common component allowing generalization pattern family applied FHT pattern algorithm yield exactly Thetan2log n FHT asymptotics might suggest actual classical Hough transform circuit could smaller size Thetan3 log n Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,238
40,238
['Salim Arslan']
1802.06772v1
In this thesis, we present robust and fully-automated methods for the subdivision of the entire human cerebral cortex based on connectivity information. Our contributions are four-fold: First, we propose a clustering approach to delineate a cortical parcellation that provides a reliable abstraction of the brain's functional organisation. Second, we cast the parcellation problem as a feature reduction problem and make use of manifold learning and image segmentation techniques to identify cortical regions with distinct structural connectivity patterns. Third, we present a multi-layer graphical model that combines within- and between-subject connectivity, which is then decomposed into a cortical parcellation that can represent the whole population, while accounting for the variability across subjects. Finally, we conduct a large-scale, systematic comparison of existing parcellation methods, with a focus on providing some insight into the reliability of brain parcellations in terms of reflecting the underlying connectivity, as well as, revealing their impact on network analysis. We evaluate the proposed parcellation methods on publicly available data from the Human Connectome Project and a plethora of quantitative and qualitative evaluation techniques investigated in the literature. Experiments across multiple resolutions demonstrate the accuracy of the presented methods at both subject and group levels with regards to reproducibility and fidelity to the data. The neuro-biological interpretation of the proposed parcellations is also investigated by comparing parcel boundaries with well-structured properties of the cerebral cortex. Results show the advantage of connectivity-driven parcellations over traditional approaches in terms of better fitting the underlying connectivity.
Connectivity-Driven Parcellation Methods for the Human Cerebral Cortex
2,018
http://arxiv.org/pdf/1802.06772v1
Title ConnectivityDriven Parcellation Methods Human Cerebral Cortex Summary thesis present robust fullyautomated method subdivision entire human cerebral cortex based connectivity information contribution fourfold First propose clustering approach delineate cortical parcellation provides reliable abstraction brain functional organisation Second cast parcellation problem feature reduction problem make use manifold learning image segmentation technique identify cortical region distinct structural connectivity pattern Third present multilayer graphical model combine within betweensubject connectivity decomposed cortical parcellation represent whole population accounting variability across subject Finally conduct largescale systematic comparison existing parcellation method focus providing insight reliability brain parcellation term reflecting underlying connectivity well revealing impact network analysis evaluate proposed parcellation method publicly available data Human Connectome Project plethora quantitative qualitative evaluation technique investigated literature Experiments across multiple resolution demonstrate accuracy presented method subject group level regard reproducibility fidelity data neurobiological interpretation proposed parcellation also investigated comparing parcel boundary wellstructured property cerebral cortex Results show advantage connectivitydriven parcellation traditional approach term better fitting underlying connectivity Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,239
40,239
['Yunfei Teng', 'Anna Choromanska', 'Mariusz Bojarski']
1802.06869v1
The unsupervised image-to-image translation aims at finding a mapping between the source ($A$) and target ($B$) image domains, where in many applications aligned image pairs are not available at training. This is an ill-posed learning problem since it requires inferring the joint probability distribution from marginals. Joint learning of coupled mappings $F_{AB}: A \rightarrow B$ and $F_{BA}: B \rightarrow A$ is commonly used by the state-of-the-art methods, like CycleGAN [Zhu et al., 2017], to learn this translation by introducing cycle consistency requirement to the learning problem, i.e. $F_{AB}(F_{BA}(B)) \approx B$ and $F_{BA}(F_{AB}(A)) \approx A$. Cycle consistency enforces the preservation of the mutual information between input and translated images. However, it does not explicitly enforce $F_{BA}$ to be an inverse operation to $F_{AB}$. We propose a new deep architecture that we call invertible autoencoder (InvAuto) to explicitly enforce this relation. This is done by forcing an encoder to be an inverted version of the decoder, where corresponding layers perform opposite mappings and share parameters. The mappings are constrained to be orthonormal. The resulting architecture leads to the reduction of the number of trainable parameters (up to $2$ times). We present image translation results on benchmark data sets and demonstrate state-of-the art performance of our approach. Finally, we test the proposed domain adaptation method on the task of road video conversion. We demonstrate that the videos converted with InvAuto have high quality and show that the NVIDIA neural-network-based end-to-end learning system for autonomous driving, known as PilotNet, trained on real road videos performs well when tested on the converted ones.
Invertible Autoencoder for domain adaptation
2,018
http://arxiv.org/pdf/1802.06869v1
Title Invertible Autoencoder domain adaptation Summary unsupervised imagetoimage translation aim finding mapping source target B image domain many application aligned image pair available training illposed learning problem since requires inferring joint probability distribution marginals Joint learning coupled mapping FAB rightarrow B FBA B rightarrow commonly used stateoftheart method like CycleGAN Zhu et al 2017 learn translation introducing cycle consistency requirement learning problem ie FABFBAB approx B FBAFABA approx Cycle consistency enforces preservation mutual information input translated image However explicitly enforce FBA inverse operation FAB propose new deep architecture call invertible autoencoder InvAuto explicitly enforce relation done forcing encoder inverted version decoder corresponding layer perform opposite mapping share parameter mapping constrained orthonormal resulting architecture lead reduction number trainable parameter 2 time present image translation result benchmark data set demonstrate stateofthe art performance approach Finally test proposed domain adaptation method task road video conversion demonstrate video converted InvAuto high quality show NVIDIA neuralnetworkbased endtoend learning system autonomous driving known PilotNet trained real road video performs well tested converted one Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,240
40,240
['Alex Zihao Zhu', 'Liangzhe Yuan', 'Kenneth Chaney', 'Kostas Daniilidis']
1802.06898v2
Event-based cameras have shown great promise in a variety of situations where frame based cameras suffer, such as high speed motions and high dynamic range scenes. However, developing algorithms for event measurements requires a new class of hand crafted algorithms. Deep learning has shown great success in providing model free solutions to many problems in the vision community, but existing networks have been developed with frame based images in mind, and there does not exist the wealth of labeled data for events as there does for images for supervised training. To these points, we present EV-FlowNet, a novel self-supervised deep learning pipeline for optical flow estimation for event based cameras. In particular, we introduce an image based representation of a given event stream, which is fed into a self-supervised neural network as the sole input. The corresponding grayscale images captured from the same camera at the same time as the events are then used as a supervisory signal to provide a loss function at training time, given the estimated flow from the network. We show that the resulting network is able to accurately predict optical flow from events only in a variety of different scenes, with performance competitive to image based networks. This method not only allows for accurate estimation of dense optical flow, but also provides a framework for the transfer of other self-supervised methods to the event-based domain.
EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras
2,018
http://arxiv.org/pdf/1802.06898v2
Title EVFlowNet SelfSupervised Optical Flow Estimation Eventbased Cameras Summary Eventbased camera shown great promise variety situation frame based camera suffer high speed motion high dynamic range scene However developing algorithm event measurement requires new class hand crafted algorithm Deep learning shown great success providing model free solution many problem vision community existing network developed frame based image mind exist wealth labeled data event image supervised training point present EVFlowNet novel selfsupervised deep learning pipeline optical flow estimation event based camera particular introduce image based representation given event stream fed selfsupervised neural network sole input corresponding grayscale image captured camera time event used supervisory signal provide loss function training time given estimated flow network show resulting network able accurately predict optical flow event variety different scene performance competitive image based network method allows accurate estimation dense optical flow also provides framework transfer selfsupervised method eventbased domain Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,241
40,241
['Qi Chang', 'Gene Cheung', 'Yao Zhao', 'Xiaolong Li', 'Rongrong Ni']
1802.06935v1
Reversible data hiding (RDH) is desirable in applications where both the hidden message and the cover medium need to be recovered without loss. Among many RDH approaches is prediction-error expansion (PEE), containing two steps: i) prediction of a target pixel value, and ii) embedding according to the value of prediction-error. In general, higher prediction performance leads to larger embedding capacity and/or lower signal distortion. Leveraging on recent advances in graph signal processing (GSP), we pose pixel prediction as a graph-signal restoration problem, where the appropriate edge weights of the underlying graph are computed using a similar patch searched in a semi-local neighborhood. Specifically, for each candidate patch, we first examine eigenvalues of its structure tensor to estimate its local smoothness. If sufficiently smooth, we pose a maximum a posteriori (MAP) problem using either a quadratic Laplacian regularizer or a graph total variation (GTV) term as signal prior. While the MAP problem using the first prior has a closed-form solution, we design an efficient algorithm for the second prior using alternating direction method of multipliers (ADMM) with nested proximal gradient descent. Experimental results show that with better quality GSP-based prediction, at low capacity the visual quality of the embedded image exceeds state-of-the-art methods noticeably.
Non-Local Graph-Based Prediction For Reversible Data Hiding In Images
2,018
http://arxiv.org/pdf/1802.06935v1
Title NonLocal GraphBased Prediction Reversible Data Hiding Images Summary Reversible data hiding RDH desirable application hidden message cover medium need recovered without loss Among many RDH approach predictionerror expansion PEE containing two step prediction target pixel value ii embedding according value predictionerror general higher prediction performance lead larger embedding capacity andor lower signal distortion Leveraging recent advance graph signal processing GSP pose pixel prediction graphsignal restoration problem appropriate edge weight underlying graph computed using similar patch searched semilocal neighborhood Specifically candidate patch first examine eigenvalue structure tensor estimate local smoothness sufficiently smooth pose maximum posteriori MAP problem using either quadratic Laplacian regularizer graph total variation GTV term signal prior MAP problem using first prior closedform solution design efficient algorithm second prior using alternating direction method multiplier ADMM nested proximal gradient descent Experimental result show better quality GSPbased prediction low capacity visual quality embedded image exceeds stateoftheart method noticeably Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,242
40,242
['Chen Wang', 'Tete Ji', 'Thien-Minh Nguyen', 'Lihua Xie']
1802.07078v2
Robust velocity and position estimation is crucial for autonomous robot navigation. The optical flow based methods for autonomous navigation have been receiving increasing attentions in tandem with the development of micro unmanned aerial vehicles. This paper proposes a kernel cross-correlator (KCC) based algorithm to determine optical flow using a monocular camera, which is named as correlation flow (CF). Correlation flow is able to provide reliable and accurate velocity estimation and is robust to motion blur. In addition, it can also estimate the altitude velocity and yaw rate, which are not available by traditional methods. Autonomous flight tests on a quadcopter show that correlation flow can provide robust trajectory estimation with very low processing power. The source codes are released based on the ROS framework.
Correlation Flow: Robust Optical Flow Using Kernel Cross-Correlators
2,018
http://arxiv.org/pdf/1802.07078v2
Title Correlation Flow Robust Optical Flow Using Kernel CrossCorrelators Summary Robust velocity position estimation crucial autonomous robot navigation optical flow based method autonomous navigation receiving increasing attention tandem development micro unmanned aerial vehicle paper proposes kernel crosscorrelator KCC based algorithm determine optical flow using monocular camera named correlation flow CF Correlation flow able provide reliable accurate velocity estimation robust motion blur addition also estimate altitude velocity yaw rate available traditional method Autonomous flight test quadcopter show correlation flow provide robust trajectory estimation low processing power source code released based ROS framework Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,243
40,243
['Oscar Rahnama', 'Duncan Frost', 'Ondrej Miksik', 'Philip H. S. Torr']
1802.07210v1
For many applications in low-power real-time robotics, stereo cameras are the sensors of choice for depth perception as they are typically cheaper and more versatile than their active counterparts. Their biggest drawback, however, is that they do not directly sense depth maps; instead, these must be estimated through data-intensive processes. Therefore, appropriate algorithm selection plays an important role in achieving the desired performance characteristics. Motivated by applications in space and mobile robotics, we implement and evaluate a FPGA-accelerated adaptation of the ELAS algorithm. Despite offering one of the best trade-offs between efficiency and accuracy, ELAS has only been shown to run at 1.5-3 fps on a high-end CPU. Our system preserves all intriguing properties of the original algorithm, such as the slanted plane priors, but can achieve a frame rate of 47fps whilst consuming under 4W of power. Unlike previous FPGA based designs, we take advantage of both components on the CPU/FPGA System-on-Chip to showcase the strategy necessary to accelerate more complex and computationally diverse algorithms for such low power, real-time systems.
Real-Time Dense Stereo Matching With ELAS on FPGA Accelerated Embedded Devices
2,018
http://arxiv.org/pdf/1802.07210v1
Title RealTime Dense Stereo Matching ELAS FPGA Accelerated Embedded Devices Summary many application lowpower realtime robotics stereo camera sensor choice depth perception typically cheaper versatile active counterpart biggest drawback however directly sense depth map instead must estimated dataintensive process Therefore appropriate algorithm selection play important role achieving desired performance characteristic Motivated application space mobile robotics implement evaluate FPGAaccelerated adaptation ELAS algorithm Despite offering one best tradeoff efficiency accuracy ELAS shown run 153 fps highend CPU system preserve intriguing property original algorithm slanted plane prior achieve frame rate 47fps whilst consuming 4W power Unlike previous FPGA based design take advantage component CPUFPGA SystemonChip showcase strategy necessary accelerate complex computationally diverse algorithm low power realtime system Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,244
['Vaclav Skala']
1802.07591v1
There are many practical applications based on the Least Square Error (LSE) approximation. It is based on a square error minimization 'on a vertical' axis. The LSE method is simple and easy also for analytical purposes. However, if data span is large over several magnitudes or non-linear LSE is used, severe numerical instability can be expected. The presented contribution describes a simple method for large span of data LSE computation. It is especially convenient if large span of data are to be processed, when the 'standard' pseudoinverse matrix is ill conditioned. It is actually based on a LSE solution using orthogonal basis vectors instead of orthonormal basis vectors. The presented approach has been used for a linear regression as well as for approximation using radial basis functions.
Least Square Error Method Robustness of Computation: What is not usually considered and taught
2,018
http://arxiv.org/pdf/1802.07591v1
Title Least Square Error Method Robustness Computation usually considered taught Summary many practical application based Least Square Error LSE approximation based square error minimization vertical axis LSE method simple easy also analytical purpose However data span large several magnitude nonlinear LSE used severe numerical instability expected presented contribution describes simple method large span data LSE computation especially convenient large span data processed standard pseudoinverse matrix ill conditioned actually based LSE solution using orthogonal basis vector instead orthonormal basis vector presented approach used linear regression well approximation using radial basis function Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,245
40,245
['Jacky C. K. Chow']
1802.07866v1
Outdoor maps and navigation information delivered by modern services and technologies like Google Maps and Garmin navigators have revolutionized the lifestyle of many people. Motivated by the desire for similar navigation systems for indoor usage from consumers, advertisers, emergency rescuers/responders, etc., many indoor environments such as shopping malls, museums, casinos, airports, transit stations, offices, and schools need to be mapped. Typically, the environment is first reconstructed by capturing many point clouds from various stations and defining their spatial relationships. Currently, there is a lack of an accurate, rigorous, and speedy method for relating point clouds in indoor, urban, satellite-denied environments. This thesis presents a novel and automatic way for fusing calibrated point clouds obtained using a terrestrial laser scanner and the Microsoft Kinect by integrating them with a low-cost inertial measurement unit. The developed system, titled the Scannect, is the first joint static-kinematic indoor 3D mapper.
Multi-Sensor Integration for Indoor 3D Reconstruction
2,018
http://arxiv.org/pdf/1802.07866v1
Title MultiSensor Integration Indoor 3D Reconstruction Summary Outdoor map navigation information delivered modern service technology like Google Maps Garmin navigator revolutionized lifestyle many people Motivated desire similar navigation system indoor usage consumer advertiser emergency rescuersresponders etc many indoor environment shopping mall museum casino airport transit station office school need mapped Typically environment first reconstructed capturing many point cloud various station defining spatial relationship Currently lack accurate rigorous speedy method relating point cloud indoor urban satellitedenied environment thesis present novel automatic way fusing calibrated point cloud obtained using terrestrial laser scanner Microsoft Kinect integrating lowcost inertial measurement unit developed system titled Scannect first joint statickinematic indoor 3D mapper Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,246
40,246
['Borja Bovcon', 'Rok Mandeljc', 'Janez Perš', 'Matej Kristan']
1802.07956v1
A new obstacle detection algorithm for unmanned surface vehicles (USVs) is presented. A state-of-the-art graphical model for semantic segmentation is extended to incorporate boat pitch and roll measurements from the on-board inertial measurement unit (IMU), and a stereo verification algorithm that consolidates tentative detections obtained from the segmentation is proposed. The IMU readings are used to estimate the location of horizon line in the image, which automatically adjusts the priors in the probabilistic semantic segmentation model. We derive the equations for projecting the horizon into images, propose an efficient optimization algorithm for the extended graphical model, and offer a practical IMU-camera-USV calibration procedure. Using an USV equipped with multiple synchronized sensors, we captured a new challenging multi-modal dataset, and annotated its images with water edge and obstacles. Experimental results show that the proposed algorithm significantly outperforms the state of the art, with nearly 30% improvement in water-edge detection accuracy, an over 21% reduction of false positive rate, an almost 60% reduction of false negative rate, and an over 65% increase of true positive rate, while its Matlab implementation runs in real-time.
Stereo obstacle detection for unmanned surface vehicles by IMU-assisted semantic segmentation
2,018
http://arxiv.org/pdf/1802.07956v1
Title Stereo obstacle detection unmanned surface vehicle IMUassisted semantic segmentation Summary new obstacle detection algorithm unmanned surface vehicle USVs presented stateoftheart graphical model semantic segmentation extended incorporate boat pitch roll measurement onboard inertial measurement unit IMU stereo verification algorithm consolidates tentative detection obtained segmentation proposed IMU reading used estimate location horizon line image automatically adjusts prior probabilistic semantic segmentation model derive equation projecting horizon image propose efficient optimization algorithm extended graphical model offer practical IMUcameraUSV calibration procedure Using USV equipped multiple synchronized sensor captured new challenging multimodal dataset annotated image water edge obstacle Experimental result show proposed algorithm significantly outperforms state art nearly 30 improvement wateredge detection accuracy 21 reduction false positive rate almost 60 reduction false negative rate 65 increase true positive rate Matlab implementation run realtime Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,247
40,247
['Hang Su', 'Varun Jampani', 'Deqing Sun', 'Subhransu Maji', 'Evangelos Kalogerakis', 'Ming-Hsuan Yang', 'Jan Kautz']
1802.08275v2
We present a network architecture for processing point clouds that directly operates on the collection of points represented as a sparse set of samples in a high-dimensional lattice. Naively applying convolutions on this lattice scales poorly both in terms of memory and computational cost as the size of the lattice increases. Instead, our network uses sparse bilateral convolutional layers as building blocks. These layers maintain efficiency by using indexing structures to apply convolutions only on occupied parts of the lattice, and allow flexible specification of the lattice structure enabling hierarchical and spatially-aware feature learning, as well as joint 2D-3D reasoning. Both point-based and image-based representations can be easily incorporated in a network with such layers and the resulting model can be trained in an end-to-end manner. We present results on 3D segmentation tasks where our approach outperforms existing state-of-the-art techniques.
SPLATNet: Sparse Lattice Networks for Point Cloud Processing
2,018
http://arxiv.org/pdf/1802.08275v2
Title SPLATNet Sparse Lattice Networks Point Cloud Processing Summary present network architecture processing point cloud directly operates collection point represented sparse set sample highdimensional lattice Naively applying convolution lattice scale poorly term memory computational cost size lattice increase Instead network us sparse bilateral convolutional layer building block layer maintain efficiency using indexing structure apply convolution occupied part lattice allow flexible specification lattice structure enabling hierarchical spatiallyaware feature learning well joint 2D3D reasoning pointbased imagebased representation easily incorporated network layer resulting model trained endtoend manner present result 3D segmentation task approach outperforms existing stateoftheart technique Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,248
40,248
['Agostino Martinelli']
1802.08515v1
This paper considers the problem of visual-inertial sensor fusion in the cooperative case and it provides new theoretical contributions, which regard its observability and its resolvability in closed form. The case of two agents is investigated. Each agent is equipped with inertial sensors (accelerometer and gyroscope) and with a monocular camera. By using the monocular camera, each agent can observe the other agent. No additional camera observations (e.g., of external point features in the environment) are considered. All the inertial sensors are assumed to be affected by a bias. First, the entire observable state is analytically derived. This state includes the absolute scale, the relative velocity between the two agents, the three Euler angles that express the rotation between the two agent frames and all the accelerometer and gyroscope biases. Second, the paper provides the extension of the closed-form solution given in [19] (which holds for a single agent) to the aforementioned cooperative case. The impact of the presence of the bias on the performance of this closed-form solution is investigated. As in the case of a single agent, this performance is significantly sensitive to the presence of a bias on the gyroscope, while, the presence of a bias on the accelerometer is negligible. Finally, a simple and effective method to obtain the gyroscope bias is proposed. Extensive simulations clearly show that the proposed method is successful. It is amazing that, it is possible to automatically retrieve the absolute scale and simultaneously calibrate the gyroscopes not only without any prior knowledge (as in [13]), but also without external point features in the environment.
Closed-form solution to cooperative visual-inertial structure from motion
2,018
http://arxiv.org/pdf/1802.08515v1
Title Closedform solution cooperative visualinertial structure motion Summary paper considers problem visualinertial sensor fusion cooperative case provides new theoretical contribution regard observability resolvability closed form case two agent investigated agent equipped inertial sensor accelerometer gyroscope monocular camera using monocular camera agent observe agent additional camera observation eg external point feature environment considered inertial sensor assumed affected bias First entire observable state analytically derived state includes absolute scale relative velocity two agent three Euler angle express rotation two agent frame accelerometer gyroscope bias Second paper provides extension closedform solution given 19 hold single agent aforementioned cooperative case impact presence bias performance closedform solution investigated case single agent performance significantly sensitive presence bias gyroscope presence bias accelerometer negligible Finally simple effective method obtain gyroscope bias proposed Extensive simulation clearly show proposed method successful amazing possible automatically retrieve absolute scale simultaneously calibrate gyroscope without prior knowledge 13 also without external point feature environment Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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['Utsav B. Gewali', 'Sildomar T. Monteiro', 'Eli Saber']
1802.08701v1
Hyperspectral sensors enable the study of the chemical properties of scene materials remotely for the purpose of identification, detection, and chemical composition analysis of objects in the environment. Hence, hyperspectral images captured from earth observing satellites and aircraft have been increasingly important in agriculture, environmental monitoring, urban planning, mining, and defense. Machine learning algorithms due to their outstanding predictive power have become a key tool for modern hyperspectral image analysis. Therefore, a solid understanding of machine learning techniques have become essential for remote sensing researchers and practitioners. This paper reviews and compares recent machine learning-based hyperspectral image analysis methods published in literature. We organize the methods by the image analysis task and by the type of machine learning algorithm, and present a two-way mapping between the image analysis tasks and the types of machine learning algorithms that can be applied to them. The paper is comprehensive in coverage of both hyperspectral image analysis tasks and machine learning algorithms. The image analysis tasks considered are land cover classification, target detection, unmixing, and physical parameter estimation. The machine learning algorithms covered are Gaussian models, linear regression, logistic regression, support vector machines, Gaussian mixture model, latent linear models, sparse linear models, Gaussian mixture models, ensemble learning, directed graphical models, undirected graphical models, clustering, Gaussian processes, Dirichlet processes, and deep learning. We also discuss the open challenges in the field of hyperspectral image analysis and explore possible future directions.
Machine learning based hyperspectral image analysis: A survey
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http://arxiv.org/pdf/1802.08701v1
Title Machine learning based hyperspectral image analysis survey Summary Hyperspectral sensor enable study chemical property scene material remotely purpose identification detection chemical composition analysis object environment Hence hyperspectral image captured earth observing satellite aircraft increasingly important agriculture environmental monitoring urban planning mining defense Machine learning algorithm due outstanding predictive power become key tool modern hyperspectral image analysis Therefore solid understanding machine learning technique become essential remote sensing researcher practitioner paper review compare recent machine learningbased hyperspectral image analysis method published literature organize method image analysis task type machine learning algorithm present twoway mapping image analysis task type machine learning algorithm applied paper comprehensive coverage hyperspectral image analysis task machine learning algorithm image analysis task considered land cover classification target detection unmixing physical parameter estimation machine learning algorithm covered Gaussian model linear regression logistic regression support vector machine Gaussian mixture model latent linear model sparse linear model Gaussian mixture model ensemble learning directed graphical model undirected graphical model clustering Gaussian process Dirichlet process deep learning also discus open challenge field hyperspectral image analysis explore possible future direction Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,250
['Amirhossein Jabalameli', 'Nabil Ettehadi', 'Aman Behal']
1802.08753v1
In this paper, we investigate the problem of grasping novel objects in unstructured environments. To address this problem, consideration of the object geometry, reachability and force closure analysis are required. We propose a framework for grasping unknown objects by localizing contact regions on the contours formed by a set of depth edges in a single view 2D depth image. According to the edge geometric features obtained from analyzing the data of the depth map, the contact regions are determined. Finally,We validate the performance of the approach by applying it to the scenes with both single and multiple objects, using Baxter manipulator.
Edge-Based Recognition of Novel Objects for Robotic Grasping
2,018
http://arxiv.org/pdf/1802.08753v1
Title EdgeBased Recognition Novel Objects Robotic Grasping Summary paper investigate problem grasping novel object unstructured environment address problem consideration object geometry reachability force closure analysis required propose framework grasping unknown object localizing contact region contour formed set depth edge single view 2D depth image According edge geometric feature obtained analyzing data depth map contact region determined FinallyWe validate performance approach applying scene single multiple object using Baxter manipulator Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,251
40,251
['Gabriele Angeletti', 'Barbara Caputo', 'Tatiana Tommasi']
1802.08833v1
A commercial robot, trained by its manufacturer to recognize a predefined number and type of objects, might be used in many settings, that will in general differ in their illumination conditions, background, type and degree of clutter, and so on. Recent computer vision works tackle this generalization issue through domain adaptation methods, assuming as source the visual domain where the system is trained and as target the domain of deployment. All approaches assume to have access to images from all classes of the target during training, an unrealistic condition in robotics applications. We address this issue proposing an algorithm that takes into account the specific needs of robot vision. Our intuition is that the nature of the domain shift experienced mostly in robotics is local. We exploit this through the learning of maps that spatially ground the domain and quantify the degree of shift, embedded into an end-to-end deep domain adaptation architecture. By explicitly localizing the roots of the domain shift we significantly reduce the number of parameters of the architecture to tune, we gain the flexibility necessary to deal with subset of categories in the target domain at training time, and we provide a clear feedback on the rationale behind any classification decision, which can be exploited in human-robot interactions. Experiments on two different settings of the iCub World database confirm the suitability of our method for robot vision.
Adaptive Deep Learning through Visual Domain Localization
2,018
http://arxiv.org/pdf/1802.08833v1
Title Adaptive Deep Learning Visual Domain Localization Summary commercial robot trained manufacturer recognize predefined number type object might used many setting general differ illumination condition background type degree clutter Recent computer vision work tackle generalization issue domain adaptation method assuming source visual domain system trained target domain deployment approach assume access image class target training unrealistic condition robotics application address issue proposing algorithm take account specific need robot vision intuition nature domain shift experienced mostly robotics local exploit learning map spatially ground domain quantify degree shift embedded endtoend deep domain adaptation architecture explicitly localizing root domain shift significantly reduce number parameter architecture tune gain flexibility necessary deal subset category target domain training time provide clear feedback rationale behind classification decision exploited humanrobot interaction Experiments two different setting iCub World database confirm suitability method robot vision Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,252
40,252
['Gabriele Partel', 'Giorgia Milli', 'Carolina Wählby']
1802.08894v1
Image-based sequencing of mRNA makes it possible to see where in a tissue sample a given gene is active, and thus discern large numbers of different cell types in parallel. This is crucial for gaining a better understanding of tissue development and disease such as cancer. Signals are collected over multiple staining and imaging cycles, and signal density together with noise makes signal decoding challenging. Previous approaches have led to low signal recall in efforts to maintain high sensitivity. We propose an approach where signal candidates are generously included, and true-signal probability at the cycle level is self-learned using a convolutional neural network. Signal candidates and probability predictions are thereafter fed into a graphical model searching for signal candidates across sequencing cycles. The graphical model combines intensity, probability and spatial distance to find optimal paths representing decoded signal sequences. We evaluate our approach in relation to state-of-the-art, and show that we increase recall by $27\%$ at maintained sensitivity. Furthermore, visual examination shows that most of the now correctly resolved signals were previously lost due to high signal density. Thus, the proposed approach has the potential to significantly improve further analysis of spatial statistics in in situ sequencing experiments.
Improving Recall of In Situ Sequencing by Self-Learned Features and a Graphical Model
2,018
http://arxiv.org/pdf/1802.08894v1
Title Improving Recall Situ Sequencing SelfLearned Features Graphical Model Summary Imagebased sequencing mRNA make possible see tissue sample given gene active thus discern large number different cell type parallel crucial gaining better understanding tissue development disease cancer Signals collected multiple staining imaging cycle signal density together noise make signal decoding challenging Previous approach led low signal recall effort maintain high sensitivity propose approach signal candidate generously included truesignal probability cycle level selflearned using convolutional neural network Signal candidate probability prediction thereafter fed graphical model searching signal candidate across sequencing cycle graphical model combine intensity probability spatial distance find optimal path representing decoded signal sequence evaluate approach relation stateoftheart show increase recall 27 maintained sensitivity Furthermore visual examination show correctly resolved signal previously lost due high signal density Thus proposed approach potential significantly improve analysis spatial statistic situ sequencing experiment Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,253
40,253
['Andres Milioto', 'Cyrill Stachniss']
1802.08960v1
The ability to interpret a scene is an important capability for a robot that is supposed to interact with its environment. The knowledge of what is in front of the robot is, for example, key to navigation, manipulation, or planning. Semantic segmentation labels each pixel of an image with a class label and thus provides a detailed semantic annotation of the surroundings to the robot. Convolutional neural networks (CNNs) became popular methods for addressing this type of problem. The available software for training and the integration of CNNs in real robots, however, is quite fragmented and difficult to use for non-experts, despite the availability of several high-quality open-source frameworks for neural network implementation and training. In this paper, we propose a novel framework called Bonnet, which addresses this fragmentation problem. It provides a modular approach to simplify the training of a semantic segmentation CNN independently of the used dataset and the intended task. Furthermore, we also address the deployment on a real robotic platform. Thus, we do not propose a new CNN approach in this paper. Instead, we provide a stable and easy-to-use tool to make this technology more approachable in the context of autonomous systems. In this sense, we aim at closing a gap between computer vision research and its use in robotics research. We provide an open-source framework for training and deployment. The training interface is implemented in Python using TensorFlow and the deployment interface provides a C++ library that can be easily integrated in an existing robotics codebase, a ROS node, and two standalone applications for label prediction in images and videos.
Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs
2,018
http://arxiv.org/pdf/1802.08960v1
Title Bonnet OpenSource Training Deployment Framework Semantic Segmentation Robotics using CNNs Summary ability interpret scene important capability robot supposed interact environment knowledge front robot example key navigation manipulation planning Semantic segmentation label pixel image class label thus provides detailed semantic annotation surroundings robot Convolutional neural network CNNs became popular method addressing type problem available software training integration CNNs real robot however quite fragmented difficult use nonexperts despite availability several highquality opensource framework neural network implementation training paper propose novel framework called Bonnet address fragmentation problem provides modular approach simplify training semantic segmentation CNN independently used dataset intended task Furthermore also address deployment real robotic platform Thus propose new CNN approach paper Instead provide stable easytouse tool make technology approachable context autonomous system sense aim closing gap computer vision research use robotics research provide opensource framework training deployment training interface implemented Python using TensorFlow deployment interface provides C library easily integrated existing robotics codebase ROS node two standalone application label prediction image video Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,254
['Jian Kang', 'Marco Körner', 'Yuanyuan Wang', 'Hannes Taubenböck', 'Xiao Xiang Zhu']
1802.09026v1
Land-use classification based on spaceborne or aerial remote sensing images has been extensively studied over the past decades. Such classification is usually a patch-wise or pixel-wise labeling over the whole image. But for many applications, such as urban population density mapping or urban utility planning, a classification map based on individual buildings is much more informative. However, such semantic classification still poses some fundamental challenges, for example, how to retrieve fine boundaries of individual buildings. In this paper, we proposed a general framework for classifying the functionality of individual buildings. The proposed method is based on Convolutional Neural Networks (CNNs) which classify facade structures from street view images, such as Google StreetView, in addition to remote sensing images which usually only show roof structures. Geographic information was utilized to mask out individual buildings, and to associate the corresponding street view images. We created a benchmark dataset which was used for training and evaluating CNNs. In addition, the method was applied to generate building classification maps on both region and city scales of several cities in Canada and the US. Keywords: CNN, Building instance classification, Street view images, OpenStreetMap
Building Instance Classification Using Street View Images
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http://arxiv.org/pdf/1802.09026v1
Title Building Instance Classification Using Street View Images Summary Landuse classification based spaceborne aerial remote sensing image extensively studied past decade classification usually patchwise pixelwise labeling whole image many application urban population density mapping urban utility planning classification map based individual building much informative However semantic classification still pose fundamental challenge example retrieve fine boundary individual building paper proposed general framework classifying functionality individual building proposed method based Convolutional Neural Networks CNNs classify facade structure street view image Google StreetView addition remote sensing image usually show roof structure Geographic information utilized mask individual building associate corresponding street view image created benchmark dataset used training evaluating CNNs addition method applied generate building classification map region city scale several city Canada US Keywords CNN Building instance classification Street view image OpenStreetMap Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,255
['Dominik Schlegel', 'Giorgio Grisetti']
1802.09261v2
Reliable and efficient Visual Place Recognition is a major building block of modern SLAM systems. Leveraging on our prior work, in this paper we present a Hamming Distance embedding Binary Search Tree (HBST) approach for binary Descriptor Matching and Image Retrieval. HBST allows for descriptor Search and Insertion in logarithmic time by exploiting particular properties of binary Feature descriptors. We support the idea behind our search structure with a thorough analysis on the exploited descriptor properties and their effects on completeness and complexity of search and insertion. To validate our claims we conducted comparative experiments for HBST and several state-of-the-art methods on a broad range of publicly available datasets. HBST is available as a compact open-source C++ header-only library.
HBST: A Hamming Distance embedding Binary Search Tree for Visual Place Recognition
2,018
http://arxiv.org/pdf/1802.09261v2
Title HBST Hamming Distance embedding Binary Search Tree Visual Place Recognition Summary Reliable efficient Visual Place Recognition major building block modern SLAM system Leveraging prior work paper present Hamming Distance embedding Binary Search Tree HBST approach binary Descriptor Matching Image Retrieval HBST allows descriptor Search Insertion logarithmic time exploiting particular property binary Feature descriptor support idea behind search structure thorough analysis exploited descriptor property effect completeness complexity search insertion validate claim conducted comparative experiment HBST several stateoftheart method broad range publicly available datasets HBST available compact opensource C headeronly library Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,256
40,256
['Parv Parkhiya', 'Rishabh Khawad', 'J. Krishna Murthy', 'Brojeshwar Bhowmick', 'K. Madhava Krishna']
1802.09292v1
We present a new paradigm for real-time object-oriented SLAM with a monocular camera. Contrary to previous approaches, that rely on object-level models, we construct category-level models from CAD collections which are now widely available. To alleviate the need for huge amounts of labeled data, we develop a rendering pipeline that enables synthesis of large datasets from a limited amount of manually labeled data. Using data thus synthesized, we learn category-level models for object deformations in 3D, as well as discriminative object features in 2D. These category models are instance-independent and aid in the design of object landmark observations that can be incorporated into a generic monocular SLAM framework. Where typical object-SLAM approaches usually solve only for object and camera poses, we also estimate object shape on-the-fly, allowing for a wide range of objects from the category to be present in the scene. Moreover, since our 2D object features are learned discriminatively, the proposed object-SLAM system succeeds in several scenarios where sparse feature-based monocular SLAM fails due to insufficient features or parallax. Also, the proposed category-models help in object instance retrieval, useful for Augmented Reality (AR) applications. We evaluate the proposed framework on multiple challenging real-world scenes and show --- to the best of our knowledge --- first results of an instance-independent monocular object-SLAM system and the benefits it enjoys over feature-based SLAM methods.
Constructing Category-Specific Models for Monocular Object-SLAM
2,018
http://arxiv.org/pdf/1802.09292v1
Title Constructing CategorySpecific Models Monocular ObjectSLAM Summary present new paradigm realtime objectoriented SLAM monocular camera Contrary previous approach rely objectlevel model construct categorylevel model CAD collection widely available alleviate need huge amount labeled data develop rendering pipeline enables synthesis large datasets limited amount manually labeled data Using data thus synthesized learn categorylevel model object deformation 3D well discriminative object feature 2D category model instanceindependent aid design object landmark observation incorporated generic monocular SLAM framework typical objectSLAM approach usually solve object camera pose also estimate object shape onthefly allowing wide range object category present scene Moreover since 2D object feature learned discriminatively proposed objectSLAM system succeeds several scenario sparse featurebased monocular SLAM fails due insufficient feature parallax Also proposed categorymodels help object instance retrieval useful Augmented Reality AR application evaluate proposed framework multiple challenging realworld scene show best knowledge first result instanceindependent monocular objectSLAM system benefit enjoys featurebased SLAM method Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,257
40,257
['Sarthak Sharma', 'Junaid Ahmed Ansari', 'J. Krishna Murthy', 'K. Madhava Krishna']
1802.09298v1
This paper introduces geometry and object shape and pose costs for multi-object tracking in urban driving scenarios. Using images from a monocular camera alone, we devise pairwise costs for object tracks, based on several 3D cues such as object pose, shape, and motion. The proposed costs are agnostic to the data association method and can be incorporated into any optimization framework to output the pairwise data associations. These costs are easy to implement, can be computed in real-time, and complement each other to account for possible errors in a tracking-by-detection framework. We perform an extensive analysis of the designed costs and empirically demonstrate consistent improvement over the state-of-the-art under varying conditions that employ a range of object detectors, exhibit a variety in camera and object motions, and, more importantly, are not reliant on the choice of the association framework. We also show that, by using the simplest of associations frameworks (two-frame Hungarian assignment), we surpass the state-of-the-art in multi-object-tracking on road scenes. More qualitative and quantitative results can be found at the following URL: https://junaidcs032.github.io/Geometry_ObjectShape_MOT/.
Beyond Pixels: Leveraging Geometry and Shape Cues for Online Multi-Object Tracking
2,018
http://arxiv.org/pdf/1802.09298v1
Title Beyond Pixels Leveraging Geometry Shape Cues Online MultiObject Tracking Summary paper introduces geometry object shape pose cost multiobject tracking urban driving scenario Using image monocular camera alone devise pairwise cost object track based several 3D cue object pose shape motion proposed cost agnostic data association method incorporated optimization framework output pairwise data association cost easy implement computed realtime complement account possible error trackingbydetection framework perform extensive analysis designed cost empirically demonstrate consistent improvement stateoftheart varying condition employ range object detector exhibit variety camera object motion importantly reliant choice association framework also show using simplest association framework twoframe Hungarian assignment surpass stateoftheart multiobjecttracking road scene qualitative quantitative result found following URL httpsjunaidcs032githubioGeometryObjectShapeMOT Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,258
40,258
['Can Zhao', 'Aaron Carass', 'Blake E. Dewey', 'Jerry L. Prince']
1802.09431v1
High resolution magnetic resonance~(MR) imaging~(MRI) is desirable in many clinical applications, however, there is a trade-off between resolution, speed of acquisition, and noise. It is common for MR images to have worse through-plane resolution~(slice thickness) than in-plane resolution. In these MRI images, high frequency information in the through-plane direction is not acquired, and cannot be resolved through interpolation. To address this issue, super-resolution methods have been developed to enhance spatial resolution. As an ill-posed problem, state-of-the-art super-resolution methods rely on the presence of external/training atlases to learn the transform from low resolution~(LR) images to high resolution~(HR) images. For several reasons, such HR atlas images are often not available for MRI sequences. This paper presents a self super-resolution~(SSR) algorithm, which does not use any external atlas images, yet can still resolve HR images only reliant on the acquired LR image. We use a blurred version of the input image to create training data for a state-of-the-art super-resolution deep network. The trained network is applied to the original input image to estimate the HR image. Our SSR result shows a significant improvement on through-plane resolution compared to competing SSR methods.
Self Super-Resolution for Magnetic Resonance Images using Deep Networks
2,018
http://arxiv.org/pdf/1802.09431v1
Title Self SuperResolution Magnetic Resonance Images using Deep Networks Summary High resolution magnetic resonanceMR imagingMRI desirable many clinical application however tradeoff resolution speed acquisition noise common MR image worse throughplane resolutionslice thickness inplane resolution MRI image high frequency information throughplane direction acquired cannot resolved interpolation address issue superresolution method developed enhance spatial resolution illposed problem stateoftheart superresolution method rely presence externaltraining atlas learn transform low resolutionLR image high resolutionHR image several reason HR atlas image often available MRI sequence paper present self superresolutionSSR algorithm use external atlas image yet still resolve HR image reliant acquired LR image use blurred version input image create training data stateoftheart superresolution deep network trained network applied original input image estimate HR image SSR result show significant improvement throughplane resolution compared competing SSR method Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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['Mahmood Sharif', 'Lujo Bauer', 'Michael K. Reiter']
1802.09653v1
Much research effort has been devoted to better understanding adversarial examples, which are specially crafted inputs to machine-learning models that are perceptually similar to benign inputs, but are classified differently (i.e., misclassified). Both algorithms that create adversarial examples and strategies for defending against them typically use $L_p$-norms to measure the perceptual similarity between an adversarial input and its benign original. Prior work has already shown, however, that two images need not be close to each other as measured by an $L_p$-norm to be perceptually similar. In this work, we show that nearness according to an $L_p$-norm is not just unnecessary for perceptual similarity, but is also insufficient. Specifically, focusing on datasets (CIFAR10 and MNIST), $L_p$-norms, and thresholds used in prior work, we show through 299-participant online user studies that "adversarial examples" that are closer to their benign counterparts than required by commonly used $L_p$-norm thresholds can nevertheless be perceptually different to humans from the corresponding benign examples. Namely, the perceptual distance between two images that are "near" each other according to an $L_p$-norm can be high enough that participants frequently classify the two images as representing different objects or digits. Combined with prior work, we thus demonstrate that nearness of inputs as measured by $L_p$-norms is neither necessary nor sufficient for perceptual similarity, which has implications for both creating and defending against adversarial examples.
On the Suitability of $L_p$-norms for Creating and Preventing Adversarial Examples
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http://arxiv.org/pdf/1802.09653v1
Title Suitability Lpnorms Creating Preventing Adversarial Examples Summary Much research effort devoted better understanding adversarial example specially crafted input machinelearning model perceptually similar benign input classified differently ie misclassified algorithm create adversarial example strategy defending typically use Lpnorms measure perceptual similarity adversarial input benign original Prior work already shown however two image need close measured Lpnorm perceptually similar work show nearness according Lpnorm unnecessary perceptual similarity also insufficient Specifically focusing datasets CIFAR10 MNIST Lpnorms threshold used prior work show 299participant online user study adversarial example closer benign counterpart required commonly used Lpnorm threshold nevertheless perceptually different human corresponding benign example Namely perceptual distance two image near according Lpnorm high enough participant frequently classify two image representing different object digit Combined prior work thus demonstrate nearness input measured Lpnorms neither necessary sufficient perceptual similarity implication creating defending adversarial example Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,260
40,260
['Samuel Scheidegger', 'Joachim Benjaminsson', 'Emil Rosenberg', 'Amrit Krishnan', 'Karl Granstrom']
1802.09975v1
Monocular cameras are one of the most commonly used sensors in the automotive industry for autonomous vehicles. One major drawback using a monocular camera is that it only makes observations in the two dimensional image plane and can not directly measure the distance to objects. In this paper, we aim at filling this gap by developing a multi-object tracking algorithm that takes an image as input and produces trajectories of detected objects in a world coordinate system. We solve this by using a deep neural network trained to detect and estimate the distance to objects from a single input image. The detections from a sequence of images are fed in to a state-of-the art Poisson multi-Bernoulli mixture tracking filter. The combination of the learned detector and the PMBM filter results in an algorithm that achieves 3D tracking using only mono-camera images as input. The performance of the algorithm is evaluated both in 3D world coordinates, and 2D image coordinates, using the publicly available KITTI object tracking dataset. The algorithm shows the ability to accurately track objects, correctly handle data associations, even when there is a big overlap of the objects in the image, and is one of the top performing algorithms on the KITTI object tracking benchmark. Furthermore, the algorithm is efficient, running on average close to 20 frames per second.
Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filtering
2,018
http://arxiv.org/pdf/1802.09975v1
Title MonoCamera 3D MultiObject Tracking Using Deep Learning Detections PMBM Filtering Summary Monocular camera one commonly used sensor automotive industry autonomous vehicle One major drawback using monocular camera make observation two dimensional image plane directly measure distance object paper aim filling gap developing multiobject tracking algorithm take image input produce trajectory detected object world coordinate system solve using deep neural network trained detect estimate distance object single input image detection sequence image fed stateofthe art Poisson multiBernoulli mixture tracking filter combination learned detector PMBM filter result algorithm achieves 3D tracking using monocamera image input performance algorithm evaluated 3D world coordinate 2D image coordinate using publicly available KITTI object tracking dataset algorithm show ability accurately track object correctly handle data association even big overlap object image one top performing algorithm KITTI object tracking benchmark Furthermore algorithm efficient running average close 20 frame per second Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,261
40,261
['Christoph Wick', 'Christian Reul', 'Frank Puppe']
1802.10033v1
This paper proposes a combination of a convolutional and a LSTM network to improve the accuracy of OCR on early printed books. While the standard model of line based OCR uses a single LSTM layer, we utilize a CNN- and Pooling-Layer combination in advance of an LSTM layer. Due to the higher amount of trainable parameters the performance of the network relies on a high amount of training examples to unleash its power. Hereby, the error is reduced by a factor of up to 44%, yielding a CER of 1% and below. To further improve the results we use a voting mechanism to achieve character error rates (CER) below $0.5%$. The runtime of the deep model for training and prediction of a book behaves very similar to a shallow network.
Improving OCR Accuracy on Early Printed Books using Deep Convolutional Networks
2,018
http://arxiv.org/pdf/1802.10033v1
Title Improving OCR Accuracy Early Printed Books using Deep Convolutional Networks Summary paper proposes combination convolutional LSTM network improve accuracy OCR early printed book standard model line based OCR us single LSTM layer utilize CNN PoolingLayer combination advance LSTM layer Due higher amount trainable parameter performance network relies high amount training example unleash power Hereby error reduced factor 44 yielding CER 1 improve result use voting mechanism achieve character error rate CER 05 runtime deep model training prediction book behaves similar shallow network Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,262
['Thanh-Toan Do', 'Ming Cai', 'Trung Pham', 'Ian Reid']
1802.10367v1
Detecting objects and their 6D poses from only RGB images is an important task for many robotic applications. While deep learning methods have made significant progress in visual object detection and segmentation, the object pose estimation task is still challenging. In this paper, we introduce an end-toend deep learning framework, named Deep-6DPose, that jointly detects, segments, and most importantly recovers 6D poses of object instances from a single RGB image. In particular, we extend the recent state-of-the-art instance segmentation network Mask R-CNN with a novel pose estimation branch to directly regress 6D object poses without any post-refinements. Our key technical contribution is the decoupling of pose parameters into translation and rotation so that the rotation can be regressed via a Lie algebra representation. The resulting pose regression loss is differential and unconstrained, making the training tractable. The experiments on two standard pose benchmarking datasets show that our proposed approach compares favorably with the state-of-the-art RGB-based multi-stage pose estimation methods. Importantly, due to the end-to-end architecture, Deep-6DPose is considerably faster than competing multi-stage methods, offers an inference speed of 10 fps that is well suited for robotic applications.
Deep-6DPose: Recovering 6D Object Pose from a Single RGB Image
2,018
http://arxiv.org/pdf/1802.10367v1
Title Deep6DPose Recovering 6D Object Pose Single RGB Image Summary Detecting object 6D pose RGB image important task many robotic application deep learning method made significant progress visual object detection segmentation object pose estimation task still challenging paper introduce endtoend deep learning framework named Deep6DPose jointly detects segment importantly recovers 6D pose object instance single RGB image particular extend recent stateoftheart instance segmentation network Mask RCNN novel pose estimation branch directly regress 6D object pose without postrefinements key technical contribution decoupling pose parameter translation rotation rotation regressed via Lie algebra representation resulting pose regression loss differential unconstrained making training tractable experiment two standard pose benchmarking datasets show proposed approach compare favorably stateoftheart RGBbased multistage pose estimation method Importantly due endtoend architecture Deep6DPose considerably faster competing multistage method offer inference speed 10 fps well suited robotic application Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,263
40,263
['Andreas Mang', 'Amir Gholami', 'Christos Davatzikos', 'George Biros']
1803.00058v1
PDE-constrained optimization problems find many applications in medical image analysis, for example, neuroimaging, cardiovascular imaging, and oncological imaging. We review related literature and give examples on the formulation, discretization, and numerical solution of PDE-constrained optimization problems for medical imaging. We discuss three examples. The first one is image registration. The second one is data assimilation for brain tumor patients, and the third one data assimilation in cardiovascular imaging. The image registration problem is a classical task in medical image analysis and seeks to find pointwise correspondences between two or more images. The data assimilation problems use a PDE-constrained formulation to link a biophysical model to patient-specific data obtained from medical images. The associated optimality systems turn out to be sets of nonlinear, multicomponent PDEs that are challenging to solve in an efficient way. The ultimate goal of our work is the design of inversion methods that integrate complementary data, and rigorously follow mathematical and physical principles, in an attempt to support clinical decision making. This requires reliable, high-fidelity algorithms with a short time-to-solution. This task is complicated by model and data uncertainties, and by the fact that PDE-constrained optimization problems are ill-posed in nature, and in general yield high-dimensional, severely ill-conditioned systems after discretization. These features make regularization, effective preconditioners, and iterative solvers that, in many cases, have to be implemented on distributed-memory architectures to be practical, a prerequisite. We showcase state-of-the-art techniques in scientific computing to tackle these challenges.
PDE-constrained optimization in medical image analysis
2,018
http://arxiv.org/pdf/1803.00058v1
Title PDEconstrained optimization medical image analysis Summary PDEconstrained optimization problem find many application medical image analysis example neuroimaging cardiovascular imaging oncological imaging review related literature give example formulation discretization numerical solution PDEconstrained optimization problem medical imaging discus three example first one image registration second one data assimilation brain tumor patient third one data assimilation cardiovascular imaging image registration problem classical task medical image analysis seek find pointwise correspondence two image data assimilation problem use PDEconstrained formulation link biophysical model patientspecific data obtained medical image associated optimality system turn set nonlinear multicomponent PDEs challenging solve efficient way ultimate goal work design inversion method integrate complementary data rigorously follow mathematical physical principle attempt support clinical decision making requires reliable highfidelity algorithm short timetosolution task complicated model data uncertainty fact PDEconstrained optimization problem illposed nature general yield highdimensional severely illconditioned system discretization feature make regularization effective preconditioners iterative solver many case implemented distributedmemory architecture practical prerequisite showcase stateoftheart technique scientific computing tackle challenge Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,264
40,264
['Huai-Jen Liang', 'Nitin J. Sanket', 'Cornelia Fermüller', 'Yiannis Aloimonos']
1803.00127v1
Although cluttered indoor scenes have a lot of useful high-level semantic information which can be used for mapping and localization, most Visual Odometry (VO) algorithms rely on the usage of geometric features such as points, lines and planes. Lately, driven by this idea, the joint optimization of semantic labels and obtaining odometry has gained popularity in the robotics community. The joint optimization is good for accurate results but is generally very slow. At the same time, in the vision community, direct and sparse approaches for VO have stricken the right balance between speed and accuracy. We merge the successes of these two communities and present a way to incorporate semantic information in the form of visual saliency to Direct Sparse Odometry - a highly successful direct sparse VO algorithm. We also present a framework to filter the visual saliency based on scene parsing. Our framework, SalientDSO, relies on the widely successful deep learning based approaches for visual saliency and scene parsing which drives the feature selection for obtaining highly-accurate and robust VO even in the presence of as few as 40 point features per frame. We provide extensive quantitative evaluation of SalientDSO on the ICL-NUIM and TUM monoVO datasets and show that we outperform DSO and ORB-SLAM - two very popular state-of-the-art approaches in the literature. We also collect and publicly release a CVL-UMD dataset which contains two indoor cluttered sequences on which we show qualitative evaluations. To our knowledge this is the first paper to use visual saliency and scene parsing to drive the feature selection in direct VO.
SalientDSO: Bringing Attention to Direct Sparse Odometry
2,018
http://arxiv.org/pdf/1803.00127v1
Title SalientDSO Bringing Attention Direct Sparse Odometry Summary Although cluttered indoor scene lot useful highlevel semantic information used mapping localization Visual Odometry VO algorithm rely usage geometric feature point line plane Lately driven idea joint optimization semantic label obtaining odometry gained popularity robotics community joint optimization good accurate result generally slow time vision community direct sparse approach VO stricken right balance speed accuracy merge success two community present way incorporate semantic information form visual saliency Direct Sparse Odometry highly successful direct sparse VO algorithm also present framework filter visual saliency based scene parsing framework SalientDSO relies widely successful deep learning based approach visual saliency scene parsing drive feature selection obtaining highlyaccurate robust VO even presence 40 point feature per frame provide extensive quantitative evaluation SalientDSO ICLNUIM TUM monoVO datasets show outperform DSO ORBSLAM two popular stateoftheart approach literature also collect publicly release CVLUMD dataset contains two indoor cluttered sequence show qualitative evaluation knowledge first paper use visual saliency scene parsing drive feature selection direct VO Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,265
40,265
['Xingyu Chen', 'Zhengxing Wu', 'Junzhi Yu']
1803.00197v2
Temporal object detection has attracted significant attention, but most popular detection methods can not leverage the rich temporal information in video or robotic vision. Although many different algorithms have been developed for video detection task, real-time online approaches are frequently deficient. In this paper, based on attention mechanism and convolutional long short-term memory (ConvLSTM), we propose a temporal single-shot detector (TSSD) for robotic vision. Distinct from previous methods, we take aim at temporally integrating pyramidal feature hierarchy using ConvLSTM, and design a novel structure including a high-level temporal unit as well as a low-level one (HL-TU) for multi-scale feature maps. Moreover, we develop a creative temporal analysis unit, namely, attention-aware ConvLSTM (AC-LSTM), in which a temporal attention module is specially tailored for background suppression and scale suppression while ConvLSTM temporally integrates attention-aware features. An association loss is designed for temporal coherence. Finally, our method is evaluated on ImageNet VID dataset. Extensive comparisons on the detection capability confirm or validate the superiority of the proposed approach. Consequently, the developed TSSD is fairly faster and achieves an overall competitive performance in terms of mean average precision. As a temporal, real-time, and online detector, TSSD is applicable to robot's intelligent perception.
TSSD: Temporal Single-Shot Object Detection Based on Attention-Aware LSTM
2,018
http://arxiv.org/pdf/1803.00197v2
Title TSSD Temporal SingleShot Object Detection Based AttentionAware LSTM Summary Temporal object detection attracted significant attention popular detection method leverage rich temporal information video robotic vision Although many different algorithm developed video detection task realtime online approach frequently deficient paper based attention mechanism convolutional long shortterm memory ConvLSTM propose temporal singleshot detector TSSD robotic vision Distinct previous method take aim temporally integrating pyramidal feature hierarchy using ConvLSTM design novel structure including highlevel temporal unit well lowlevel one HLTU multiscale feature map Moreover develop creative temporal analysis unit namely attentionaware ConvLSTM ACLSTM temporal attention module specially tailored background suppression scale suppression ConvLSTM temporally integrates attentionaware feature association loss designed temporal coherence Finally method evaluated ImageNet VID dataset Extensive comparison detection capability confirm validate superiority proposed approach Consequently developed TSSD fairly faster achieves overall competitive performance term mean average precision temporal realtime online detector TSSD applicable robot intelligent perception Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,266
40,266
['Daniel Barath']
1803.00260v1
We aim at estimating the fundamental matrix in two views from five correspondences of rotation invariant features obtained by e.g.\ the SIFT detector. The proposed minimal solver first estimates a homography from three correspondences assuming that they are co-planar and exploiting their rotational components. Then the fundamental matrix is obtained from the homography and two additional point pairs in general position. The proposed approach, combined with robust estimators like Graph-Cut RANSAC, is superior to other state-of-the-art algorithms both in terms of accuracy and number of iterations required. This is validated on synthesized data and $561$ real image pairs. Moreover, the tests show that requiring three points on a plane is not too restrictive in urban environment and locally optimized robust estimators lead to accurate estimates even if the points are not entirely co-planar. As a potential application, we show that using the proposed method makes two-view multi-motion estimation more accurate.
Five-point Fundamental Matrix Estimation for Uncalibrated Cameras
2,018
http://arxiv.org/pdf/1803.00260v1
Title Fivepoint Fundamental Matrix Estimation Uncalibrated Cameras Summary aim estimating fundamental matrix two view five correspondence rotation invariant feature obtained eg SIFT detector proposed minimal solver first estimate homography three correspondence assuming coplanar exploiting rotational component fundamental matrix obtained homography two additional point pair general position proposed approach combined robust estimator like GraphCut RANSAC superior stateoftheart algorithm term accuracy number iteration required validated synthesized data 561 real image pair Moreover test show requiring three point plane restrictive urban environment locally optimized robust estimator lead accurate estimate even point entirely coplanar potential application show using proposed method make twoview multimotion estimation accurate Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,267
['Ao Zhou', 'Wei Wang', 'Ni Chen', 'Edmund Y. Lam', 'Byoungho Lee', 'Guohai Situ']
1803.00395v1
Fourier ptychographi cmicroscopy(FPM) is a newly developed computational imaging technique that can provide gigapixel images with both high resolution (HR) and wide field of view (FOV). However, the positional misalignment of the LED array induces a degradation of the reconstruction, especially in the regions away from the optical axis. In this paper, we propose a robust and fast method to correct the LED misalignment of FPM, termed as misalignment correction for FPM (mcFPM). Although different regions in the FOV have different sensitivity to the LED misalignment, the experimental results show that mcFPM is robust to eliminate the degradation in each region. Compared with the state-of-the-art methods, mcFPM is much faster.
Fast and robust misalignment correction of Fourier ptychographic microscopy
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http://arxiv.org/pdf/1803.00395v1
Title Fast robust misalignment correction Fourier ptychographic microscopy Summary Fourier ptychographi cmicroscopyFPM newly developed computational imaging technique provide gigapixel image high resolution HR wide field view FOV However positional misalignment LED array induces degradation reconstruction especially region away optical axis paper propose robust fast method correct LED misalignment FPM termed misalignment correction FPM mcFPM Although different region FOV different sensitivity LED misalignment experimental result show mcFPM robust eliminate degradation region Compared stateoftheart method mcFPM much faster Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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['Mehdi Yedroudj', 'Frederic Comby', 'Marc Chaumont']
1803.00407v1
For about 10 years, detecting the presence of a secret message hidden in an image was performed with an Ensemble Classifier trained with Rich features. In recent years, studies such as Xu et al. have indicated that well-designed convolutional Neural Networks (CNN) can achieve comparable performance to the two-step machine learning approaches. In this paper, we propose a CNN that outperforms the state-ofthe-art in terms of error probability. The proposition is in the continuity of what has been recently proposed and it is a clever fusion of important bricks used in various papers. Among the essential parts of the CNN, one can cite the use of a pre-processing filterbank and a Truncation activation function, five convolutional layers with a Batch Normalization associated with a Scale Layer, as well as the use of a sufficiently sized fully connected section. An augmented database has also been used to improve the training of the CNN. Our CNN was experimentally evaluated against S-UNIWARD and WOW embedding algorithms and its performances were compared with those of three other methods: an Ensemble Classifier plus a Rich Model, and two other CNN steganalyzers.
Yedrouj-Net: An efficient CNN for spatial steganalysis
2,018
http://arxiv.org/pdf/1803.00407v1
Title YedroujNet efficient CNN spatial steganalysis Summary 10 year detecting presence secret message hidden image performed Ensemble Classifier trained Rich feature recent year study Xu et al indicated welldesigned convolutional Neural Networks CNN achieve comparable performance twostep machine learning approach paper propose CNN outperforms stateoftheart term error probability proposition continuity recently proposed clever fusion important brick used various paper Among essential part CNN one cite use preprocessing filterbank Truncation activation function five convolutional layer Batch Normalization associated Scale Layer well use sufficiently sized fully connected section augmented database also used improve training CNN CNN experimentally evaluated SUNIWARD WOW embedding algorithm performance compared three method Ensemble Classifier plus Rich Model two CNN steganalyzers Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,269
40,269
['Andre Mastmeyer']
1803.00492v2
German text, english abstract: Mortality in gynecologic cancers, including cervical, ovarian, vaginal and vulvar cancers, is more than 6% internationally [1]. In many countries external radiotherapy is supplemented by brachytherapy with high locally administered doses as standard. The superior ability of magnetic resonance imaging (MRI) to differentiate soft tissue has led to an increasing use of this imaging technique in the intraoperative planning and implementation of brachytherapy. A technical challenge associated with the use of MRI imaging for brachytherapy - in contrast to computed tomography (CT) imaging - is the dark-diffuse appearance and thus difficult identification of the catheter paths in the resulting images. This problem is addressed by the precise method described herein of tracing the catheters from the catheter tip. The average identification time for a single catheter path was three seconds on a standard PC. Segmentation time, accuracy and precision are promising indicators of the value of this method for the clinical application of image-guided gynecological brachytherapy. After surgery (OP), the healthy surrounding tissue of the tumor is usually irradiated. This reduces the risk of leaving behind residual cells that would likely cause a recurrence of the cancer or the formation of metastases - secondary tumors elsewhere in the body. In the case of a tumor on the cervix or prostate, the operation is minimally invasive, ie. the removal of the cancer and the irradiation are performed cost-effectively and risk-avoiding by keyhole surgery instead of open surgery.
Genaue modellbasierte Identifikation von gynäkologischen Katheterpfaden für die MRT-bildgestützte Brachytherapie
2,018
http://arxiv.org/pdf/1803.00492v2
Title Genaue modellbasierte Identifikation von gynäkologischen Katheterpfaden für die MRTbildgestützte Brachytherapie Summary German text english abstract Mortality gynecologic cancer including cervical ovarian vaginal vulvar cancer 6 internationally 1 many country external radiotherapy supplemented brachytherapy high locally administered dos standard superior ability magnetic resonance imaging MRI differentiate soft tissue led increasing use imaging technique intraoperative planning implementation brachytherapy technical challenge associated use MRI imaging brachytherapy contrast computed tomography CT imaging darkdiffuse appearance thus difficult identification catheter path resulting image problem addressed precise method described herein tracing catheter catheter tip average identification time single catheter path three second standard PC Segmentation time accuracy precision promising indicator value method clinical application imageguided gynecological brachytherapy surgery OP healthy surrounding tissue tumor usually irradiated reduces risk leaving behind residual cell would likely cause recurrence cancer formation metastasis secondary tumor elsewhere body case tumor cervix prostate operation minimally invasive ie removal cancer irradiation performed costeffectively riskavoiding keyhole surgery instead open surgery Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,270
['C. Di Ruberto', 'L. Putzu', 'G. Rodriguez']
1803.00638v1
In this work we propose a fast and stable algorithm for the computation of the orthogonal moments of an image. Indeed, the traditional orthogonal moments formulations are characterized by a high discriminative power, but also by a large computational complexity, which limits their real-time application. The recursive approach described in this paper aims to solve these limitations. In our experiments, we evaluate the effectiveness of the recursive formulations and its performance for the reconstruction task. The results show a great reduction in the computational complexity with respect to the closed form formulation, together with a greater accuracy in reconstruction. Then, in order to assess and compare the accuracy of the computed moments in texture analysis, we perform classification experiments on six well-known databases of texture images. Again, the recursive formulation performs better in classification than the closed form representation. More importantly, if computed from the GLCM of the image, the new moments outperform significantly some of the most diffused state-of-the-art descriptors for texture classification.
Fast and accurate computation of orthogonal moments for texture analysis
2,018
http://arxiv.org/pdf/1803.00638v1
Title Fast accurate computation orthogonal moment texture analysis Summary work propose fast stable algorithm computation orthogonal moment image Indeed traditional orthogonal moment formulation characterized high discriminative power also large computational complexity limit realtime application recursive approach described paper aim solve limitation experiment evaluate effectiveness recursive formulation performance reconstruction task result show great reduction computational complexity respect closed form formulation together greater accuracy reconstruction order ass compare accuracy computed moment texture analysis perform classification experiment six wellknown database texture image recursive formulation performs better classification closed form representation importantly computed GLCM image new moment outperform significantly diffused stateoftheart descriptor texture classification Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,271
['Anas M. Al-Oraiqat', 'E. A. Bashkov', 'V. Babkov', 'C. Titarenko']
1803.00737v1
The paper presents a parallel implementation of existing image fusion methods on a graphical cluster. Parallel implementations of methods based on discrete wavelet transformation (Haars and Daubechies discrete wavelet transform) are developed. Experiments were performed on a cluster using GPU and CPU and performance gains were estimated for the use of the developed parallel implementations to process satellite images from satellite Landsat 7. The implementation on a graphic cluster provides performance improvement from 2 to 18 times. The quality of the considered methods was evaluated by ERGAS and QNR metrics. The results show performance gains and retaining of quality with the cluster of GPU compared to the results obtained by the authors and other researchers for a CPU and single GPU.
Fusion of multispectral satellite imagery using a cluster of graphics processing unit
2,018
http://arxiv.org/pdf/1803.00737v1
Title Fusion multispectral satellite imagery using cluster graphic processing unit Summary paper present parallel implementation existing image fusion method graphical cluster Parallel implementation method based discrete wavelet transformation Haars Daubechies discrete wavelet transform developed Experiments performed cluster using GPU CPU performance gain estimated use developed parallel implementation process satellite image satellite Landsat 7 implementation graphic cluster provides performance improvement 2 18 time quality considered method evaluated ERGAS QNR metric result show performance gain retaining quality cluster GPU compared result obtained author researcher CPU single GPU Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,272
40,272
['Pilailuck Panphattarasap', 'Andrew Calway']
1803.00788v1
We describe a novel approach to image based localisation in urban environments using semantic matching between images and a 2-D map. It contrasts with the vast majority of existing approaches which use image to image database matching. We use highly compact binary descriptors to represent semantic features at locations, significantly increasing scalability compared with existing methods and having the potential for greater invariance to variable imaging conditions. The approach is also more akin to human map reading, making it more suited to human-system interaction. The binary descriptors indicate the presence or not of semantic features relating to buildings and road junctions in discrete viewing directions. We use CNN classifiers to detect the features in images and match descriptor estimates with a database of location tagged descriptors derived from the 2-D map. In isolation, the descriptors are not sufficiently discriminative, but when concatenated sequentially along a route, their combination becomes highly distinctive and allows localisation even when using non-perfect classifiers. Performance is further improved by taking into account left or right turns over a route. Experimental results obtained using Google StreetView and OpenStreetMap data show that the approach has considerable potential, achieving localisation accuracy of around 85% using routes corresponding to approximately 200 meters.
Automated Map Reading: Image Based Localisation in 2-D Maps Using Binary Semantic Descriptors
2,018
http://arxiv.org/pdf/1803.00788v1
Title Automated Map Reading Image Based Localisation 2D Maps Using Binary Semantic Descriptors Summary describe novel approach image based localisation urban environment using semantic matching image 2D map contrast vast majority existing approach use image image database matching use highly compact binary descriptor represent semantic feature location significantly increasing scalability compared existing method potential greater invariance variable imaging condition approach also akin human map reading making suited humansystem interaction binary descriptor indicate presence semantic feature relating building road junction discrete viewing direction use CNN classifier detect feature image match descriptor estimate database location tagged descriptor derived 2D map isolation descriptor sufficiently discriminative concatenated sequentially along route combination becomes highly distinctive allows localisation even using nonperfect classifier Performance improved taking account left right turn route Experimental result obtained using Google StreetView OpenStreetMap data show approach considerable potential achieving localisation accuracy around 85 using route corresponding approximately 200 meter Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,273
40,273
['Aaditya Prakash', 'Nick Moran', 'Solomon Garber', 'Antonella DiLillo', 'James Storer']
1803.00940v1
As deep neural networks (DNNs) have been integrated into critical systems, several methods to attack these systems have been developed. These adversarial attacks make imperceptible modifications to an image that fool DNN classifiers. We present an adaptive JPEG encoder which defends against many of these attacks. Experimentally, we show that our method produces images with high visual quality while greatly reducing the potency of state-of-the-art attacks. Our algorithm requires only a modest increase in encoding time, produces a compressed image which can be decompressed by an off-the-shelf JPEG decoder, and classified by an unmodified classifier
Protecting JPEG Images Against Adversarial Attacks
2,018
http://arxiv.org/pdf/1803.00940v1
Title Protecting JPEG Images Adversarial Attacks Summary deep neural network DNNs integrated critical system several method attack system developed adversarial attack make imperceptible modification image fool DNN classifier present adaptive JPEG encoder defends many attack Experimentally show method produce image high visual quality greatly reducing potency stateoftheart attack algorithm requires modest increase encoding time produce compressed image decompressed offtheshelf JPEG decoder classified unmodified classifier Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,274
40,274
['Guodong Du', 'Liang Yuan', 'Kong Joo Shin', 'Shunsuke Managi']
1803.01159v1
The neighborhood effect is a key driving factor for the land-use change (LUC) process. This study applies convolutional neural networks (CNN) to capture neighborhood characteristics from satellite images and to enhance the performance of LUC modeling. We develop a hybrid CNN model (conv-net) to predict the LU transition probability by combining satellite images and geographical features. A spatial weight layer is designed to incorporate the distance-decay characteristics of neighborhood effect into conv-net. As an alternative model, we also develop a hybrid convolutional denoising autoencoder and multi-layer perceptron model (CDAE-net), which specifically learns latent representations from satellite images and denoises the image data. Finally, a DINAMICA-based cellular automata (CA) model simulates the LU pattern. The results show that the convolutional-based models improve the modeling performances compared with a model that accepts only the geographical features. Overall, conv-net outperforms CDAE-net in terms of LUC predictive performance. Nonetheless, CDAE-net performs better when the data are noisy.
Enhancement of land-use change modeling using convolutional neural networks and convolutional denoising autoencoders
2,018
http://arxiv.org/pdf/1803.01159v1
Title Enhancement landuse change modeling using convolutional neural network convolutional denoising autoencoders Summary neighborhood effect key driving factor landuse change LUC process study applies convolutional neural network CNN capture neighborhood characteristic satellite image enhance performance LUC modeling develop hybrid CNN model convnet predict LU transition probability combining satellite image geographical feature spatial weight layer designed incorporate distancedecay characteristic neighborhood effect convnet alternative model also develop hybrid convolutional denoising autoencoder multilayer perceptron model CDAEnet specifically learns latent representation satellite image denoises image data Finally DINAMICAbased cellular automaton CA model simulates LU pattern result show convolutionalbased model improve modeling performance compared model accepts geographical feature Overall convnet outperforms CDAEnet term LUC predictive performance Nonetheless CDAEnet performs better data noisy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,275
40,275
['Dongwon Park', 'Se Young Chun']
1803.01356v1
Robotic grasp detection task is still challenging, particularly for novel objects. With the recent advance of deep learning, there have been several works on detecting robotic grasp using neural networks. Typically, regression based grasp detection methods have outperformed classification based detection methods in computation complexity with excellent accuracy. However, classification based robotic grasp detection still seems to have merits such as intermediate step observability and straightforward back propagation routine for end-to-end training. In this work, we propose a novel classification based robotic grasp detection method with multiple-stage spatial transformer networks (STN). Our proposed method was able to achieve state-of-the-art performance in accuracy with real- time computation. Additionally, unlike other regression based grasp detection methods, our proposed method allows partial observation for intermediate results such as grasp location and orientation for a number of grasp configuration candidates.
Classification based Grasp Detection using Spatial Transformer Network
2,018
http://arxiv.org/pdf/1803.01356v1
Title Classification based Grasp Detection using Spatial Transformer Network Summary Robotic grasp detection task still challenging particularly novel object recent advance deep learning several work detecting robotic grasp using neural network Typically regression based grasp detection method outperformed classification based detection method computation complexity excellent accuracy However classification based robotic grasp detection still seems merit intermediate step observability straightforward back propagation routine endtoend training work propose novel classification based robotic grasp detection method multiplestage spatial transformer network STN proposed method able achieve stateoftheart performance accuracy real time computation Additionally unlike regression based grasp detection method proposed method allows partial observation intermediate result grasp location orientation number grasp configuration candidate Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,276
40,276
['Yuhua Chen', 'Feng Shi', 'Anthony G. Christodoulou', 'Zhengwei Zhou', 'Yibin Xie', 'Debiao Li']
1803.01417v2
High-resolution (HR) magnetic resonance images (MRI) provide detailed anatomical information important for clinical application and quantitative image analysis. However, HR MRI conventionally comes at the cost of longer scan time, smaller spatial coverage, and lower signal-to-noise ratio (SNR). Recent studies have shown that single image super-resolution (SISR), a technique to recover HR details from one single low-resolution (LR) input image, could provide high-quality image details with the help of advanced deep convolutional neural networks (CNN). However, deep neural networks consume memory heavily and run slowly, especially in 3D settings. In this paper, we propose a novel 3D neural network design, namely a multi-level densely connected super-resolution network (mDCSRN) with generative adversarial network (GAN)-guided training. The mDCSRN quickly trains and inferences and the GAN promotes realistic output hardly distinguishable from original HR images. Our results from experiments on a dataset with 1,113 subjects show that our new architecture beats other popular deep learning methods in recovering 4x resolution-downgraded im-ages and runs 6x faster.
Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network
2,018
http://arxiv.org/pdf/1803.01417v2
Title Efficient Accurate MRI SuperResolution using Generative Adversarial Network 3D MultiLevel Densely Connected Network Summary Highresolution HR magnetic resonance image MRI provide detailed anatomical information important clinical application quantitative image analysis However HR MRI conventionally come cost longer scan time smaller spatial coverage lower signaltonoise ratio SNR Recent study shown single image superresolution SISR technique recover HR detail one single lowresolution LR input image could provide highquality image detail help advanced deep convolutional neural network CNN However deep neural network consume memory heavily run slowly especially 3D setting paper propose novel 3D neural network design namely multilevel densely connected superresolution network mDCSRN generative adversarial network GANguided training mDCSRN quickly train inference GAN promotes realistic output hardly distinguishable original HR image result experiment dataset 1113 subject show new architecture beat popular deep learning method recovering 4x resolutiondowngraded image run 6x faster Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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['Oliver Moolan-Feroze', 'Andrew Calway']
1803.01577v1
In this work we present a novel framework that uses deep learning to predict object feature points that are out-of-view in the input image. This system was developed with the application of model-based tracking in mind, particularly in the case of autonomous inspection robots, where only partial views of the object are available. Out-of-view prediction is enabled by applying scaling to the feature point labels during network training. This is combined with a recurrent neural network architecture designed to provide the final prediction layers with rich feature information from across the spatial extent of the input image. To show the versatility of these out-of-view predictions, we describe how to integrate them in both a particle filter tracker and an optimisation based tracker. To evaluate our work we compared our framework with one that predicts only points inside the image. We show that as the amount of the object in view decreases, being able to predict outside the image bounds adds robustness to the final pose estimation.
Predicting Out-of-View Feature Points for Model-Based Camera Pose Estimation
2,018
http://arxiv.org/pdf/1803.01577v1
Title Predicting OutofView Feature Points ModelBased Camera Pose Estimation Summary work present novel framework us deep learning predict object feature point outofview input image system developed application modelbased tracking mind particularly case autonomous inspection robot partial view object available Outofview prediction enabled applying scaling feature point label network training combined recurrent neural network architecture designed provide final prediction layer rich feature information across spatial extent input image show versatility outofview prediction describe integrate particle filter tracker optimisation based tracker evaluate work compared framework one predicts point inside image show amount object view decrease able predict outside image bound add robustness final pose estimation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,278
['Stanley L. Tuznik', 'Peter J. Olver', 'Allen Tannenbaum']
1803.01669v2
Image feature points are detected as pixels which locally maximize a detector function, two commonly used examples of which are the (Euclidean) image gradient and the Harris-Stephens corner detector. A major limitation of these feature detectors are that they are only Euclidean-invariant. In this work we demonstrate the application of a 2D affine-invariant image feature point detector based on differential invariants as derived through the equivariant method of moving frames. The fundamental equi-affine differential invariants for 3D image volumes are also computed.
Affine Differential Invariants for Invariant Feature Point Detection
2,018
http://arxiv.org/pdf/1803.01669v2
Title Affine Differential Invariants Invariant Feature Point Detection Summary Image feature point detected pixel locally maximize detector function two commonly used example Euclidean image gradient HarrisStephens corner detector major limitation feature detector Euclideaninvariant work demonstrate application 2D affineinvariant image feature point detector based differential invariant derived equivariant method moving frame fundamental equiaffine differential invariant 3D image volume also computed Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,279
40,279
['Junaid Ahmed Ansari', 'Sarthak Sharma', 'Anshuman Majumdar', 'J. Krishna Murthy', 'K. Madhava Krishna']
1803.02057v1
Accurate localization of other traffic participants is a vital task in autonomous driving systems. State-of-the-art systems employ a combination of sensing modalities such as RGB cameras and LiDARs for localizing traffic participants, but most such demonstrations have been confined to plain roads. We demonstrate, to the best of our knowledge, the first results for monocular object localization and shape estimation on surfaces that do not share the same plane with the moving monocular camera. We approximate road surfaces by local planar patches and use semantic cues from vehicles in the scene to initialize a local bundle-adjustment like procedure that simultaneously estimates the pose and shape of the vehicles, and the orientation of the local ground plane on which the vehicle stands as well. We evaluate the proposed approach on the KITTI and SYNTHIA-SF benchmarks, for a variety of road plane configurations. The proposed approach significantly improves the state-of-the-art for monocular object localization on arbitrarily-shaped roads.
The Earth ain't Flat: Monocular Reconstruction of Vehicles on Steep and Graded Roads from a Moving Camera
2,018
http://arxiv.org/pdf/1803.02057v1
Title Earth aint Flat Monocular Reconstruction Vehicles Steep Graded Roads Moving Camera Summary Accurate localization traffic participant vital task autonomous driving system Stateoftheart system employ combination sensing modality RGB camera LiDARs localizing traffic participant demonstration confined plain road demonstrate best knowledge first result monocular object localization shape estimation surface share plane moving monocular camera approximate road surface local planar patch use semantic cue vehicle scene initialize local bundleadjustment like procedure simultaneously estimate pose shape vehicle orientation local ground plane vehicle stand well evaluate proposed approach KITTI SYNTHIASF benchmark variety road plane configuration proposed approach significantly improves stateoftheart monocular object localization arbitrarilyshaped road Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,280
40,280
['Cristóvão Cruz', 'Alessandro Foi', 'Vladimir Katkovnik', 'Karen Egiazarian']
1803.02112v1
We introduce a paradigm for nonlocal sparsity reinforced deep convolutional neural network denoising. It is a combination of a local multiscale denoising by a convolutional neural network (CNN) based denoiser and a nonlocal denoising based on a nonlocal filter (NLF) exploiting the mutual similarities between groups of patches. CNN models are leveraged with noise levels that progressively decrease at every iteration of our framework, while their output is regularized by a nonlocal prior implicit within the NLF. Unlike complicated neural networks that embed the nonlocality prior within the layers of the network, our framework is modular, it uses standard pre-trained CNNs together with standard nonlocal filters. An instance of the proposed framework, called NN3D, is evaluated over large grayscale image datasets showing state-of-the-art performance.
Nonlocality-Reinforced Convolutional Neural Networks for Image Denoising
2,018
http://arxiv.org/pdf/1803.02112v1
Title NonlocalityReinforced Convolutional Neural Networks Image Denoising Summary introduce paradigm nonlocal sparsity reinforced deep convolutional neural network denoising combination local multiscale denoising convolutional neural network CNN based denoiser nonlocal denoising based nonlocal filter NLF exploiting mutual similarity group patch CNN model leveraged noise level progressively decrease every iteration framework output regularized nonlocal prior implicit within NLF Unlike complicated neural network embed nonlocality prior within layer network framework modular us standard pretrained CNNs together standard nonlocal filter instance proposed framework called NN3D evaluated large grayscale image datasets showing stateoftheart performance Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,281
40,281
['Christian A. Mueller', 'Andreas Birk']
1803.02140v1
A topological shape analysis is proposed and utilized to learn concepts that reflect shape commonalities. Our approach is two-fold: i) a spatial topology analysis of point cloud segment constellations within objects. Therein constellations are decomposed and described in an hierarchical manner - from single segments to segment groups until a single group reflects an entire object. ii) a topology analysis of the description space in which segment decompositions are exposed in. Inspired by Persistent Homology, hidden groups of shape commonalities are revealed from object segment decompositions. Experiments show that extracted persistent groups of commonalities can represent semantically meaningful shape concepts. We also show the generalization capability of the proposed approach considering samples of external datasets.
Conceptualization of Object Compositions Using Persistent Homology
2,018
http://arxiv.org/pdf/1803.02140v1
Title Conceptualization Object Compositions Using Persistent Homology Summary topological shape analysis proposed utilized learn concept reflect shape commonality approach twofold spatial topology analysis point cloud segment constellation within object Therein constellation decomposed described hierarchical manner single segment segment group single group reflects entire object ii topology analysis description space segment decomposition exposed Inspired Persistent Homology hidden group shape commonality revealed object segment decomposition Experiments show extracted persistent group commonality represent semantically meaningful shape concept also show generalization capability proposed approach considering sample external datasets Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,282
40,282
['Xinwen Zhou', 'Xuguang Lan', 'Hanbo Zhang', 'Zhiqiang Tian', 'Yang Zhang', 'Nanning Zheng']
1803.02209v1
In this paper, we present a real-time approach to predict multiple grasping poses for a parallel-plate robotic gripper using RGB images. A model with oriented anchor box mechanism is proposed and a new matching strategy is used during the training process. An end-to-end fully convolutional neural network is employed in our work. The network consists of two parts: the feature extractor and multi-grasp predictor. The feature extractor is a deep convolutional neural network. The multi-grasp predictor regresses grasp rectangles from predefined oriented rectangles, called oriented anchor boxes, and classifies the rectangles into graspable and ungraspable. On the standard Cornell Grasp Dataset, our model achieves an accuracy of 97.74% and 96.61% on image-wise split and object-wise split respectively, and outperforms the latest state-of-the-art approach by 1.74% on image-wise split and 0.51% on object-wise split.
Fully Convolutional Grasp Detection Network with Oriented Anchor Box
2,018
http://arxiv.org/pdf/1803.02209v1
Title Fully Convolutional Grasp Detection Network Oriented Anchor Box Summary paper present realtime approach predict multiple grasping pose parallelplate robotic gripper using RGB image model oriented anchor box mechanism proposed new matching strategy used training process endtoend fully convolutional neural network employed work network consists two part feature extractor multigrasp predictor feature extractor deep convolutional neural network multigrasp predictor regress grasp rectangle predefined oriented rectangle called oriented anchor box classifies rectangle graspable ungraspable standard Cornell Grasp Dataset model achieves accuracy 9774 9661 imagewise split objectwise split respectively outperforms latest stateoftheart approach 174 imagewise split 051 objectwise split Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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['Sergey Triputen', 'Atmaraaj Gopal', 'Thomas Weber', 'Christian Hofert', 'Kristiaan Schreve', 'Matthias Ratsch']
1803.02257v1
SLAM systems are mainly applied for robot navigation while research on feasibility for motion planning with SLAM for tasks like bin-picking, is scarce. Accurate 3D reconstruction of objects and environments is important for planning motion and computing optimal gripper pose to grasp objects. In this work, we propose the methods to analyze the accuracy of a 3D environment reconstructed using a LSD-SLAM system with a monocular camera mounted onto the gripper of a collaborative robot. We discuss and propose a solution to the pose space conversion problem. Finally, we present several criteria to analyze the 3D reconstruction accuracy. These could be used as guidelines to improve the accuracy of 3D reconstructions with monocular LSD-SLAM and other SLAM based solutions.
Methodology to analyze the accuracy of 3D objects reconstructed with collaborative robot based monocular LSD-SLAM
2,018
http://arxiv.org/pdf/1803.02257v1
Title Methodology analyze accuracy 3D object reconstructed collaborative robot based monocular LSDSLAM Summary SLAM system mainly applied robot navigation research feasibility motion planning SLAM task like binpicking scarce Accurate 3D reconstruction object environment important planning motion computing optimal gripper pose grasp object work propose method analyze accuracy 3D environment reconstructed using LSDSLAM system monocular camera mounted onto gripper collaborative robot discus propose solution pose space conversion problem Finally present several criterion analyze 3D reconstruction accuracy could used guideline improve accuracy 3D reconstruction monocular LSDSLAM SLAM based solution Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,284
40,284
['Demetris Marnerides', 'Thomas Bashford-Rogers', 'Jonathan Hatchett', 'Kurt Debattista']
1803.02266v1
High dynamic range (HDR) imaging provides the capability of handling real world lighting as opposed to the traditional low dynamic range (LDR) which struggles to accurately represent images with higher dynamic range. However, most imaging content is still available only in LDR. This paper presents a method for generating HDR content from LDR content based on deep Convolutional Neural Networks (CNNs) termed ExpandNet. ExpandNet accepts LDR images as input and generates images with an expanded range in an end-to-end fashion. The model attempts to reconstruct missing information that was lost from the original signal due to quantization, clipping, tone mapping or gamma correction. The added information is reconstructed from learned features, as the network is trained in a supervised fashion using a dataset of HDR images. The approach is fully automatic and data driven; it does not require any heuristics or human expertise. ExpandNet uses a multiscale architecture which avoids the use of upsampling layers to improve image quality. The method performs well compared to expansion/inverse tone mapping operators quantitatively on multiple metrics, even for badly exposed inputs.
ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content
2,018
http://arxiv.org/pdf/1803.02266v1
Title ExpandNet Deep Convolutional Neural Network High Dynamic Range Expansion Low Dynamic Range Content Summary High dynamic range HDR imaging provides capability handling real world lighting opposed traditional low dynamic range LDR struggle accurately represent image higher dynamic range However imaging content still available LDR paper present method generating HDR content LDR content based deep Convolutional Neural Networks CNNs termed ExpandNet ExpandNet accepts LDR image input generates image expanded range endtoend fashion model attempt reconstruct missing information lost original signal due quantization clipping tone mapping gamma correction added information reconstructed learned feature network trained supervised fashion using dataset HDR image approach fully automatic data driven require heuristic human expertise ExpandNet us multiscale architecture avoids use upsampling layer improve image quality method performs well compared expansioninverse tone mapping operator quantitatively multiple metric even badly exposed input Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,285
40,285
['Hanz Cuevas-Velasquez', 'Nanbo Li', 'Radim Tylecek', 'Marcelo Saval-Calvo', 'Robert B. Fisher']
1803.02285v1
Visual servoing is a well-known task in robotics. However, there are still challenges when multiple sources are combined to accurately guide the robot or occlusions appear. In this paper we present a novel visual servoing approach using hybrid multi-camera input data to lead a robot arm accurately to dynamically moving target points in the presence of partial occlusions. The approach uses four RGBD sensors as Eye-to-Hand (EtoH) visual input, and an arm-mounted stereo camera as Eye-in-Hand (EinH). A Master supervisor task selects between using the EtoH or the EinH, depending on the distance between the robot and target. The Master also selects the subset of EtoH cameras that best perceive the target. When the EinH sensor is used, if the target becomes occluded or goes out of the sensor's view-frustrum, the Master switches back to the EtoH sensors to re-track the object. Using this adaptive visual input data, the robot is then controlled using an iterative planner that uses position, orientation and joint configuration to estimate the trajectory. Since the target is dynamic, this trajectory is updated every time-step. Experiments show good performance in three different situations: tracking a ball, targeting a bulls-eye, and guiding a straw to a mouth. The experiments cover both simple situations such as the ball that is completely visible from all cameras, and more complex situations such as the mouth which is partially occluded from some of the sensors.
Hybrid Multi-camera Visual Servoing to Moving Target
2,018
http://arxiv.org/pdf/1803.02285v1
Title Hybrid Multicamera Visual Servoing Moving Target Summary Visual servoing wellknown task robotics However still challenge multiple source combined accurately guide robot occlusion appear paper present novel visual servoing approach using hybrid multicamera input data lead robot arm accurately dynamically moving target point presence partial occlusion approach us four RGBD sensor EyetoHand EtoH visual input armmounted stereo camera EyeinHand EinH Master supervisor task selects using EtoH EinH depending distance robot target Master also selects subset EtoH camera best perceive target EinH sensor used target becomes occluded go sensor viewfrustrum Master switch back EtoH sensor retrack object Using adaptive visual input data robot controlled using iterative planner us position orientation joint configuration estimate trajectory Since target dynamic trajectory updated every timestep Experiments show good performance three different situation tracking ball targeting bullseye guiding straw mouth experiment cover simple situation ball completely visible camera complex situation mouth partially occluded sensor Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,286
40,286
['Cheng Zhao', 'Li Sun', 'Pulak Purkait', 'Tom Duckett', 'Rustam Stolkin']
1803.02286v1
This paper introduces a fully deep learning approach to monocular SLAM, which can perform simultaneous localization using a neural network for learning visual odometry (L-VO) and dense 3D mapping. Dense 2D flow and a depth image are generated from monocular images by sub-networks, which are then used by a 3D flow associated layer in the L-VO network to generate dense 3D flow. Given this 3D flow, the dual-stream L-VO network can then predict the 6DOF relative pose and furthermore reconstruct the vehicle trajectory. In order to learn the correlation between motion directions, the Bivariate Gaussian modelling is employed in the loss function. The L-VO network achieves an overall performance of 2.68% for average translational error and 0.0143 deg/m for average rotational error on the KITTI odometry benchmark. Moreover, the learned depth is fully leveraged to generate a dense 3D map. As a result, an entire visual SLAM system, that is, learning monocular odometry combined with dense 3D mapping, is achieved.
Learning monocular visual odometry with dense 3D mapping from dense 3D flow
2,018
http://arxiv.org/pdf/1803.02286v1
Title Learning monocular visual odometry dense 3D mapping dense 3D flow Summary paper introduces fully deep learning approach monocular SLAM perform simultaneous localization using neural network learning visual odometry LVO dense 3D mapping Dense 2D flow depth image generated monocular image subnetworks used 3D flow associated layer LVO network generate dense 3D flow Given 3D flow dualstream LVO network predict 6DOF relative pose furthermore reconstruct vehicle trajectory order learn correlation motion direction Bivariate Gaussian modelling employed loss function LVO network achieves overall performance 268 average translational error 00143 degm average rotational error KITTI odometry benchmark Moreover learned depth fully leveraged generate dense 3D map result entire visual SLAM system learning monocular odometry combined dense 3D mapping achieved Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,287
40,287
['Pedro F. Proença', 'Yang Gao']
1803.02380v2
This paper presents CAPE, a method to extract planes and cylinder segments from organized point clouds, which processes 640x480 depth images on a single CPU core at an average of 300 Hz, by operating on a grid of planar cells. While, compared to state-of-the-art plane extraction, the latency of CAPE is more consistent and 4-10 times faster, depending on the scene, we also demonstrate empirically that applying CAPE to visual odometry can improve trajectory estimation on scenes made of cylindrical surfaces (e.g. tunnels), whereas using a plane extraction approach that is not curve-aware deteriorates performance on these scenes. To use these geometric primitives in visual odometry, we propose extending a probabilistic RGB-D odometry framework based on points, lines and planes to cylinder primitives. Following this framework, CAPE runs on fused depth maps and the parameters of cylinders are modelled probabilistically to account for uncertainty and weight accordingly the pose optimization residuals.
Fast Cylinder and Plane Extraction from Depth Cameras for Visual Odometry
2,018
http://arxiv.org/pdf/1803.02380v2
Title Fast Cylinder Plane Extraction Depth Cameras Visual Odometry Summary paper present CAPE method extract plane cylinder segment organized point cloud process 640x480 depth image single CPU core average 300 Hz operating grid planar cell compared stateoftheart plane extraction latency CAPE consistent 410 time faster depending scene also demonstrate empirically applying CAPE visual odometry improve trajectory estimation scene made cylindrical surface eg tunnel whereas using plane extraction approach curveaware deteriorates performance scene use geometric primitive visual odometry propose extending probabilistic RGBD odometry framework based point line plane cylinder primitive Following framework CAPE run fused depth map parameter cylinder modelled probabilistically account uncertainty weight accordingly pose optimization residual Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,288
['Christian Zimmermann', 'Tim Welschehold', 'Christian Dornhege', 'Wolfram Burgard', 'Thomas Brox']
1803.02622v2
We propose an approach to estimate 3D human pose in real world units from a single RGBD image and show that it exceeds performance of monocular 3D pose estimation approaches from color as well as pose estimation exclusively from depth. Our approach builds on robust human keypoint detectors for color images and incorporates depth for lifting into 3D. We combine the system with our learning from demonstration framework to instruct a service robot without the need of markers. Experiments in real world settings demonstrate that our approach enables a PR2 robot to imitate manipulation actions observed from a human teacher.
3D Human Pose Estimation in RGBD Images for Robotic Task Learning
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http://arxiv.org/pdf/1803.02622v2
Title 3D Human Pose Estimation RGBD Images Robotic Task Learning Summary propose approach estimate 3D human pose real world unit single RGBD image show exceeds performance monocular 3D pose estimation approach color well pose estimation exclusively depth approach build robust human keypoint detector color image incorporates depth lifting 3D combine system learning demonstration framework instruct service robot without need marker Experiments real world setting demonstrate approach enables PR2 robot imitate manipulation action observed human teacher Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,289
40,289
['Yoshikatsu Nakajima', 'Keisuke Tateno', 'Federico Tombari', 'Hideo Saito']
1803.02784v1
We propose an efficient and scalable method for incrementally building a dense, semantically annotated 3D map in real-time. The proposed method assigns class probabilities to each region, not each element (e.g., surfel and voxel), of the 3D map which is built up through a robust SLAM framework and incrementally segmented with a geometric-based segmentation method. Differently from all other approaches, our method has a capability of running at over 30Hz while performing all processing components, including SLAM, segmentation, 2D recognition, and updating class probabilities of each segmentation label at every incoming frame, thanks to the high efficiency that characterizes the computationally intensive stages of our framework. By utilizing a specifically designed CNN to improve the frame-wise segmentation result, we can also achieve high accuracy. We validate our method on the NYUv2 dataset by comparing with the state of the art in terms of accuracy and computational efficiency, and by means of an analysis in terms of time and space complexity.
Fast and Accurate Semantic Mapping through Geometric-based Incremental Segmentation
2,018
http://arxiv.org/pdf/1803.02784v1
Title Fast Accurate Semantic Mapping Geometricbased Incremental Segmentation Summary propose efficient scalable method incrementally building dense semantically annotated 3D map realtime proposed method assigns class probability region element eg surfel voxel 3D map built robust SLAM framework incrementally segmented geometricbased segmentation method Differently approach method capability running 30Hz performing processing component including SLAM segmentation 2D recognition updating class probability segmentation label every incoming frame thanks high efficiency characterizes computationally intensive stage framework utilizing specifically designed CNN improve framewise segmentation result also achieve high accuracy validate method NYUv2 dataset comparing state art term accuracy computational efficiency mean analysis term time space complexity Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,290
40,290
['Donatella Firmani', 'Marco Maiorino', 'Paolo Merialdo', 'Elena Nieddu']
1803.03200v1
In Codice Ratio is a research project to study tools and techniques for analyzing the contents of historical documents conserved in the Vatican Secret Archives (VSA). In this paper, we present our efforts to develop a system to support the transcription of medieval manuscripts. The goal is to provide paleographers with a tool to reduce their efforts in transcribing large volumes, as those stored in the VSA, producing good transcriptions for significant portions of the manuscripts. We propose an original approach based on character segmentation. Our solution is able to deal with the dirty segmentation that inevitably occurs in handwritten documents. We use a convolutional neural network to recognize characters and language models to compose word transcriptions. Our approach requires minimal training efforts, making the transcription process more scalable as the production of training sets requires a few pages and can be easily crowdsourced. We have conducted experiments on manuscripts from the Vatican Registers, an unreleased corpus containing the correspondence of the popes. With training data produced by 120 high school students, our system has been able to produce good transcriptions that can be used by paleographers as a solid basis to speedup the transcription process at a large scale.
Towards Knowledge Discovery from the Vatican Secret Archives. In Codice Ratio -- Episode 1: Machine Transcription of the Manuscripts
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http://arxiv.org/pdf/1803.03200v1
Title Towards Knowledge Discovery Vatican Secret Archives Codice Ratio Episode 1 Machine Transcription Manuscripts Summary Codice Ratio research project study tool technique analyzing content historical document conserved Vatican Secret Archives VSA paper present effort develop system support transcription medieval manuscript goal provide paleographer tool reduce effort transcribing large volume stored VSA producing good transcription significant portion manuscript propose original approach based character segmentation solution able deal dirty segmentation inevitably occurs handwritten document use convolutional neural network recognize character language model compose word transcription approach requires minimal training effort making transcription process scalable production training set requires page easily crowdsourced conducted experiment manuscript Vatican Registers unreleased corpus containing correspondence pope training data produced 120 high school student system able produce good transcription used paleographer solid basis speedup transcription process large scale Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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['Shaowei Jiang', 'Kaikai Guo', 'Jun Liao', 'Guoan Zheng']
1803.03434v1
Fourier ptychography is a recently developed imaging approach for large field-of-view and high-resolution microscopy. Here we model the Fourier ptychographic forward imaging process using a convolution neural network (CNN) and recover the complex object information in the network training process. In this approach, the input of the network is the point spread function in the spatial domain or the coherent transfer function in the Fourier domain. The object is treated as 2D learnable weights of a convolution or a multiplication layer. The output of the network is modeled as the loss function we aim to minimize. The batch size of the network corresponds to the number of captured low-resolution images in one forward / backward pass. We use a popular open-source machine learning library, TensorFlow, for setting up the network and conducting the optimization process. We analyze the performance of different learning rates, different solvers, and different batch sizes. It is shown that a large batch size with the Adam optimizer achieves the best performance in general. To accelerate the phase retrieval process, we also discuss a strategy to implement Fourier-magnitude projection using a multiplication neural network model. Since convolution and multiplication are the two most-common operations in imaging modeling, the reported approach may provide a new perspective to examine many coherent and incoherent systems. As a demonstration, we discuss the extensions of the reported networks for modeling single-pixel imaging and structured illumination microscopy (SIM). 4-frame resolution doubling is demonstrated using a neural network for SIM. We have made our implementation code open-source for the broad research community.
Solving Fourier ptychographic imaging problems via neural network modeling and TensorFlow
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http://arxiv.org/pdf/1803.03434v1
Title Solving Fourier ptychographic imaging problem via neural network modeling TensorFlow Summary Fourier ptychography recently developed imaging approach large fieldofview highresolution microscopy model Fourier ptychographic forward imaging process using convolution neural network CNN recover complex object information network training process approach input network point spread function spatial domain coherent transfer function Fourier domain object treated 2D learnable weight convolution multiplication layer output network modeled loss function aim minimize batch size network corresponds number captured lowresolution image one forward backward pas use popular opensource machine learning library TensorFlow setting network conducting optimization process analyze performance different learning rate different solver different batch size shown large batch size Adam optimizer achieves best performance general accelerate phase retrieval process also discus strategy implement Fouriermagnitude projection using multiplication neural network model Since convolution multiplication two mostcommon operation imaging modeling reported approach may provide new perspective examine many coherent incoherent system demonstration discus extension reported network modeling singlepixel imaging structured illumination microscopy SIM 4frame resolution doubling demonstrated using neural network SIM made implementation code opensource broad research community Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,292
40,292
['Oluwarotimi Giwa', 'Abdsamad Benkrid']
1803.03828v1
Colour analysis is a crucial step in image-based fire detection algorithms. Many of the proposed fire detection algorithms in a still image are prone to false alarms caused by objects with a colour similar to fire. To design a colour-based system with a better false alarm rate, a new colour-differentiating conversion matrix, efficient on images of high colour complexity, is proposed. The elements of this conversion matrix are obtained by performing K-medoids clustering and Particle Swarm Optimisation procedures on a fire sample image with a background of high fire-colour similarity. The proposed conversion matrix is then used to construct two new fire colour detection frameworks. The first detection method is a two-stage non-linear image transformation framework, while the second is a direct transformation of an image with the proposed conversion matrix. A performance comparison of the proposed methods with alternate methods in the literature was carried out. Experimental results indicate that the linear image transformation method outperforms other methods regarding false alarm rate while the non-linear two-stage image transformation method has the best performance on the F-score metric and provides a better trade-off between missed detection and false alarm rate.
Fire detection in a still image using colour information
2,018
http://arxiv.org/pdf/1803.03828v1
Title Fire detection still image using colour information Summary Colour analysis crucial step imagebased fire detection algorithm Many proposed fire detection algorithm still image prone false alarm caused object colour similar fire design colourbased system better false alarm rate new colourdifferentiating conversion matrix efficient image high colour complexity proposed element conversion matrix obtained performing Kmedoids clustering Particle Swarm Optimisation procedure fire sample image background high firecolour similarity proposed conversion matrix used construct two new fire colour detection framework first detection method twostage nonlinear image transformation framework second direct transformation image proposed conversion matrix performance comparison proposed method alternate method literature carried Experimental result indicate linear image transformation method outperforms method regarding false alarm rate nonlinear twostage image transformation method best performance Fscore metric provides better tradeoff missed detection false alarm rate Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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['Arda Senocak', 'Tae-Hyun Oh', 'Junsik Kim', 'Ming-Hsuan Yang', 'In So Kweon']
1803.03849v1
Visual events are usually accompanied by sounds in our daily lives. We pose the question: Can the machine learn the correspondence between visual scene and the sound, and localize the sound source only by observing sound and visual scene pairs like human? In this paper, we propose a novel unsupervised algorithm to address the problem of localizing the sound source in visual scenes. A two-stream network structure which handles each modality, with attention mechanism is developed for sound source localization. Moreover, although our network is formulated within the unsupervised learning framework, it can be extended to a unified architecture with a simple modification for the supervised and semi-supervised learning settings as well. Meanwhile, a new sound source dataset is developed for performance evaluation. Our empirical evaluation shows that the unsupervised method eventually go through false conclusion in some cases. We show that even with a few supervision, false conclusion is able to be corrected and the source of sound in a visual scene can be localized effectively.
Learning to Localize Sound Source in Visual Scenes
2,018
http://arxiv.org/pdf/1803.03849v1
Title Learning Localize Sound Source Visual Scenes Summary Visual event usually accompanied sound daily life pose question machine learn correspondence visual scene sound localize sound source observing sound visual scene pair like human paper propose novel unsupervised algorithm address problem localizing sound source visual scene twostream network structure handle modality attention mechanism developed sound source localization Moreover although network formulated within unsupervised learning framework extended unified architecture simple modification supervised semisupervised learning setting well Meanwhile new sound source dataset developed performance evaluation empirical evaluation show unsupervised method eventually go false conclusion case show even supervision false conclusion able corrected source sound visual scene localized effectively Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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['Navaneeth Kamballur Kottayil', 'Giuseppe Valenzise', 'Frederic Dufaux', 'Irene Cheng']
1803.04053v1
Accurate prediction of local distortion visibility thresholds is critical in many image and video processing applications. Existing methods require an accurate modeling of the human visual system, and are derived through pshycophysical experiments with simple, artificial stimuli. These approaches, however, are difficult to generalize to natural images with complex types of distortion. In this paper, we explore a different perspective, and we investigate whether it is possible to learn local distortion visibility from image quality scores. We propose a convolutional neural network based optimization framework to infer local detection thresholds in a distorted image. Our model is trained on multiple quality datasets, and the results are correlated with empirical visibility thresholds collected on complex stimuli in a recent study. Our results are comparable to state-of-the-art mathematical models that were trained on phsycovisual data directly. This suggests that it is possible to predict psychophysical phenomena from visibility information embedded in image quality scores.
Learning Local Distortion Visibility From Image Quality Data-sets
2,018
http://arxiv.org/pdf/1803.04053v1
Title Learning Local Distortion Visibility Image Quality Datasets Summary Accurate prediction local distortion visibility threshold critical many image video processing application Existing method require accurate modeling human visual system derived pshycophysical experiment simple artificial stimulus approach however difficult generalize natural image complex type distortion paper explore different perspective investigate whether possible learn local distortion visibility image quality score propose convolutional neural network based optimization framework infer local detection threshold distorted image model trained multiple quality datasets result correlated empirical visibility threshold collected complex stimulus recent study result comparable stateoftheart mathematical model trained phsycovisual data directly suggests possible predict psychophysical phenomenon visibility information embedded image quality score Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,295
40,295
['Ignacio Alvarez Illan', 'Javier Ramirez', 'Juan M. Gorriz', 'Katja Pinker', 'Anke Meyer-Baese']
1803.04200v1
Non-mass enhancing lesions (NME) constitute a diagnostic challenge in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of the breast. Computer Aided Diagnosis (CAD) systems provide physicians with advanced tools for analysis, assessment and evaluation that have a significant impact on the diagnostic performance. Here, we propose a new approach for the specific problem of NME detection and segmentation, by taking advantage of independent component analysis (ICA) to extract a data-driven dynamic characterization of tissue. A set of independent sources was obtained from a dataset of patients, and the dynamic behavior of the different tissues was described by multiple dynamic curves, together with a set of eigenimages describing the scores for each voxel. A new test image is projected onto the independent source space using the unmixing matrix, and each voxel is classified by a support vector machine (SVM) that has already been trained with manually delineated data. A solution to the high false positive rate problem is proposed by controlling the SVM hyperplane location. The CAD system is trained and validated, reaching a DSC coefficient of 0.7215 for NME segmentation.
Automated non-mass enhancing lesion detection and segmentation in breast DCE-MRI
2,018
http://arxiv.org/pdf/1803.04200v1
Title Automated nonmass enhancing lesion detection segmentation breast DCEMRI Summary Nonmass enhancing lesion NME constitute diagnostic challenge dynamic contrast enhanced magnetic resonance imaging DCEMRI breast Computer Aided Diagnosis CAD system provide physician advanced tool analysis assessment evaluation significant impact diagnostic performance propose new approach specific problem NME detection segmentation taking advantage independent component analysis ICA extract datadriven dynamic characterization tissue set independent source obtained dataset patient dynamic behavior different tissue described multiple dynamic curve together set eigenimages describing score voxel new test image projected onto independent source space using unmixing matrix voxel classified support vector machine SVM already trained manually delineated data solution high false positive rate problem proposed controlling SVM hyperplane location CAD system trained validated reaching DSC coefficient 07215 NME segmentation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,296
40,296
['Tsun-Hsuan Wang', 'Hung-Jui Huang', 'Juan-Ting Lin', 'Chan-Wei Hu', 'Kuo-Hao Zeng', 'Min Sun']
1803.04228v1
$ $Visual place recognition is challenging, especially when only a few place exemplars are given. To mitigate the challenge, we consider place recognition method using omnidirectional cameras and propose a novel Omnidirectional Convolutional Neural Network (O-CNN) to handle severe camera pose variation. Given a visual input, the task of the O-CNN is not to retrieve the matched place exemplar, but to retrieve the closest place exemplar and estimate the relative distance between the input and the closest place. With the ability to estimate relative distance, a heuristic policy is proposed to navigate a robot to the retrieved closest place. Note that the network is designed to take advantage of the omnidirectional view by incorporating circular padding and rotation invariance. To train a powerful O-CNN, we build a virtual world for training on a large scale. We also propose a continuous lifted structured feature embedding loss to learn the concept of distance efficiently. Finally, our experimental results confirm that our method achieves state-of-the-art accuracy and speed with both the virtual world and real-world datasets.
Omnidirectional CNN for Visual Place Recognition and Navigation
2,018
http://arxiv.org/pdf/1803.04228v1
Title Omnidirectional CNN Visual Place Recognition Navigation Summary Visual place recognition challenging especially place exemplar given mitigate challenge consider place recognition method using omnidirectional camera propose novel Omnidirectional Convolutional Neural Network OCNN handle severe camera pose variation Given visual input task OCNN retrieve matched place exemplar retrieve closest place exemplar estimate relative distance input closest place ability estimate relative distance heuristic policy proposed navigate robot retrieved closest place Note network designed take advantage omnidirectional view incorporating circular padding rotation invariance train powerful OCNN build virtual world training large scale also propose continuous lifted structured feature embedding loss learn concept distance efficiently Finally experimental result confirm method achieves stateoftheart accuracy speed virtual world realworld datasets Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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40,297
['Remi Cura', 'Julien Perret', 'Nicolas Paparoditis']
1803.04332v1
World population is raising, especially the part of people living in cities. With increased population and complex roles regarding their inhabitants and their surroundings, cities concentrate difficulties for design, planning and analysis. These tasks require a way to reconstruct/model a city. Traditionally, much attention has been given to buildings reconstruction, yet an essential part of city were neglected: streets. Streets reconstruction has been seldom researched. Streets are also complex compositions of urban features, and have a unique role for transportation (as they comprise roads). We aim at completing the recent state of the art for building reconstruction (Musialski2012) by considering all other aspect of urban reconstruction. We introduce the need for city models. Because reconstruction always necessitates data, we first analyse which data are available. We then expose a state of the art of street reconstruction, street network reconstruction, urban features reconstruction/modelling, vegetation , and urban objects reconstruction/modelling. Although reconstruction strategies vary widely, we can order them by the role the model plays, from data driven approach, to model-based approach, to inverse procedural modelling and model catalogue matching. The main challenges seems to come from the complex nature of urban environment and from the limitations of the available data. Urban features have strong relationships, between them, and to their surrounding, as well as in hierarchical relations. Procedural modelling has the power to express these relations, and could be applied to the reconstruction of urban features via the Inverse Procedural Modelling paradigm.
A state of the art of urban reconstruction: street, street network, vegetation, urban feature
2,018
http://arxiv.org/pdf/1803.04332v1
Title state art urban reconstruction street street network vegetation urban feature Summary World population raising especially part people living city increased population complex role regarding inhabitant surroundings city concentrate difficulty design planning analysis task require way reconstructmodel city Traditionally much attention given building reconstruction yet essential part city neglected street Streets reconstruction seldom researched Streets also complex composition urban feature unique role transportation comprise road aim completing recent state art building reconstruction Musialski2012 considering aspect urban reconstruction introduce need city model reconstruction always necessitates data first analyse data available expose state art street reconstruction street network reconstruction urban feature reconstructionmodelling vegetation urban object reconstructionmodelling Although reconstruction strategy vary widely order role model play data driven approach modelbased approach inverse procedural modelling model catalogue matching main challenge seems come complex nature urban environment limitation available data Urban feature strong relationship surrounding well hierarchical relation Procedural modelling power express relation could applied reconstruction urban feature via Inverse Procedural Modelling paradigm Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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['Chetan Sai Tutika', 'Charan Vallapaneni', 'Karthik R', 'Bharath KP', 'N Ruban Rajesh Kumar Muthu']
1803.04620v1
In this paper, we develop a method to detect vacant parking spaces in an environment with unclear segments and contours with the help of MATLAB image processing capabilities. Due to the anomalies present in the parking spaces, such as uneven illumination, distorted slot lines and overlapping of cars. The present-day conventional algorithms have difficulties processing the image for accurate results. The algorithm proposed uses a combination of image pre-processing and false contour detection techniques to improve the detection efficiency. The proposed method also eliminates the need to employ individual sensors to detect a car, instead uses real-time static images to consider a group of slots together, instead of the usual single slot method. This greatly decreases the expenses required to design an efficient parking system. We compare the performance of our algorithm to that of other techniques. These comparisons show that the proposed algorithm can detect the vacancies in the parking spots while ignoring the false data and other distortions.
Image Segmentation and Processing for Efficient Parking Space Analysis
2,018
http://arxiv.org/pdf/1803.04620v1
Title Image Segmentation Processing Efficient Parking Space Analysis Summary paper develop method detect vacant parking space environment unclear segment contour help MATLAB image processing capability Due anomaly present parking space uneven illumination distorted slot line overlapping car presentday conventional algorithm difficulty processing image accurate result algorithm proposed us combination image preprocessing false contour detection technique improve detection efficiency proposed method also eliminates need employ individual sensor detect car instead us realtime static image consider group slot together instead usual single slot method greatly decrease expense required design efficient parking system compare performance algorithm technique comparison show proposed algorithm detect vacancy parking spot ignoring false data distortion Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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['Ren Yang', 'Mai Xu', 'Zulin Wang', 'Tianyi Li']
1803.04680v4
The past few years have witnessed great success in applying deep learning to enhance the quality of compressed image/video. The existing approaches mainly focus on enhancing the quality of a single frame, ignoring the similarity between consecutive frames. In this paper, we investigate that heavy quality fluctuation exists across compressed video frames, and thus low quality frames can be enhanced using the neighboring high quality frames, seen as Multi-Frame Quality Enhancement (MFQE). Accordingly, this paper proposes an MFQE approach for compressed video, as a first attempt in this direction. In our approach, we firstly develop a Support Vector Machine (SVM) based detector to locate Peak Quality Frames (PQFs) in compressed video. Then, a novel Multi-Frame Convolutional Neural Network (MF-CNN) is designed to enhance the quality of compressed video, in which the non-PQF and its nearest two PQFs are as the input. The MF-CNN compensates motion between the non-PQF and PQFs through the Motion Compensation subnet (MC-subnet). Subsequently, the Quality Enhancement subnet (QE-subnet) reduces compression artifacts of the non-PQF with the help of its nearest PQFs. Finally, the experiments validate the effectiveness and generality of our MFQE approach in advancing the state-of-the-art quality enhancement of compressed video. The code of our MFQE approach is available at https://github.com/ryangBUAA/MFQE.git
Multi-Frame Quality Enhancement for Compressed Video
2,018
http://arxiv.org/pdf/1803.04680v4
Title MultiFrame Quality Enhancement Compressed Video Summary past year witnessed great success applying deep learning enhance quality compressed imagevideo existing approach mainly focus enhancing quality single frame ignoring similarity consecutive frame paper investigate heavy quality fluctuation exists across compressed video frame thus low quality frame enhanced using neighboring high quality frame seen MultiFrame Quality Enhancement MFQE Accordingly paper proposes MFQE approach compressed video first attempt direction approach firstly develop Support Vector Machine SVM based detector locate Peak Quality Frames PQFs compressed video novel MultiFrame Convolutional Neural Network MFCNN designed enhance quality compressed video nonPQF nearest two PQFs input MFCNN compensates motion nonPQF PQFs Motion Compensation subnet MCsubnet Subsequently Quality Enhancement subnet QEsubnet reduces compression artifact nonPQF help nearest PQFs Finally experiment validate effectiveness generality MFQE approach advancing stateoftheart quality enhancement compressed video code MFQE approach available httpsgithubcomryangBUAAMFQEgit Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object
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