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['Matias Valdenegro-Toro']

1709.02600v1

Forward-looking sonar can capture high resolution images of underwater scenes, but their interpretation is complex. Generic object detection in such images has not been solved, specially in cases of small and unknown objects. In comparison, detection proposal algorithms have produced top performing object detectors in real-world color images. In this work we develop a Convolutional Neural Network that can reliably score objectness of image windows in forward-looking sonar images and by thresholding objectness, we generate detection proposals. In our dataset of marine garbage objects, we obtain 94% recall, generating around 60 proposals per image. The biggest strength of our method is that it can generalize to previously unseen objects. We show this by detecting chain links, walls and a wrench without previous training in such objects. We strongly believe our method can be used for class-independent object detection, with many real-world applications such as chain following and mine detection.

Objectness Scoring and Detection Proposals in Forward-Looking Sonar Images with Convolutional Neural Networks

http://arxiv.org/pdf/1709.02600v1

Title Objectness Scoring Detection Proposals ForwardLooking Sonar Images Convolutional Neural Networks Summary Forwardlooking sonar capture high resolution image underwater scene interpretation complex Generic object detection image solved specially case small unknown object comparison detection proposal algorithm produced top performing object detector realworld color image work develop Convolutional Neural Network reliably score objectness image window forwardlooking sonar image thresholding objectness generate detection proposal dataset marine garbage object obtain 94 recall generating around 60 proposal per image biggest strength method generalize previously unseen object show detecting chain link wall wrench without previous training object strongly believe method used classindependent object detection many realworld application chain following mine detection 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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['Longhao Yuan', 'Qibin Zhao', 'Jianting Cao']

1709.02641v2

In this paper, we aim at the completion problem of high order tensor data with missing entries. The existing tensor factorization and completion methods suffer from the curse of dimensionality when the order of tensor N>>3. To overcome this problem, we propose an efficient algorithm called TT-WOPT (Tensor-train Weighted OPTimization) to find the latent core tensors of tensor data and recover the missing entries. Tensor-train decomposition, which has the powerful representation ability with linear scalability to tensor order, is employed in our algorithm. The experimental results on synthetic data and natural image completion demonstrate that our method significantly outperforms the other related methods. Especially when the missing rate of data is very high, e.g., 85% to 99%, our algorithm can achieve much better performance than other state-of-the-art algorithms.

Completion of High Order Tensor Data with Missing Entries via Tensor-train Decomposition

http://arxiv.org/pdf/1709.02641v2

Title Completion High Order Tensor Data Missing Entries via Tensortrain Decomposition Summary paper aim completion problem high order tensor data missing entry existing tensor factorization completion method suffer curse dimensionality order tensor N3 overcome problem propose efficient algorithm called TTWOPT Tensortrain Weighted OPTimization find latent core tensor tensor data recover missing entry Tensortrain decomposition powerful representation ability linear scalability tensor order employed algorithm experimental result synthetic data natural image completion demonstrate method significantly outperforms related method Especially missing rate data high eg 85 99 algorithm achieve much better performance stateoftheart 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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['Jonathan K. George', 'Cesare Soci', 'Volker J. Sorger']

1709.02684v1

The ability to rapidly identify symmetry and anti-symmetry is an essential attribute of intelligence. Symmetry perception is a central process in human vision and may be key to human 3D visualization. While previous work in understanding neuron symmetry perception has concentrated on the neuron as an integrator, here we show how the coincidence detecting property of the spiking neuron can be used to reveal symmetry density in spatial data. We develop a method for synchronizing symmetry-identifying spiking artificial neural networks to enable layering and feedback in the network. We show a method for building a network capable of identifying symmetry density between sets of data and present a digital logic implementation demonstrating an 8x8 leaky-integrate-and-fire symmetry detector in a field programmable gate array. Our results show that the efficiencies of spiking neural networks can be harnessed to rapidly identify symmetry in spatial data with applications in image processing, 3D computer vision, and robotics.

Identifying Mirror Symmetry Density with Delay in Spiking Neural Networks

http://arxiv.org/pdf/1709.02684v1

Title Identifying Mirror Symmetry Density Delay Spiking Neural Networks Summary ability rapidly identify symmetry antisymmetry essential attribute intelligence Symmetry perception central process human vision may key human 3D visualization previous work understanding neuron symmetry perception concentrated neuron integrator show coincidence detecting property spiking neuron used reveal symmetry density spatial data develop method synchronizing symmetryidentifying spiking artificial neural network enable layering feedback network show method building network capable identifying symmetry density set data present digital logic implementation demonstrating 8x8 leakyintegrateandfire symmetry detector field programmable gate array result show efficiency spiking neural network harnessed rapidly identify symmetry spatial data application image processing 3D computer vision robotics 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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['Bolin Chen', 'Haodong Li', 'Weiqi Luo']

1709.02908v1

In recent years, image forensics has attracted more and more attention, and many forensic methods have been proposed for identifying image processing operations. Up to now, most existing methods are based on hand crafted features, and just one specific operation is considered in their methods. In many forensic scenarios, however, multiple classification for various image processing operations is more practical. Besides, it is difficult to obtain effective features by hand for some image processing operations. In this paper, therefore, we propose a new convolutional neural network (CNN) based method to adaptively learn discriminative features for identifying typical image processing operations. We carefully design the high pass filter bank to get the image residuals of the input image, the channel expansion layer to mix up the resulting residuals, the pooling layers, and the activation functions employed in our method. The extensive results show that the proposed method can outperform the currently best method based on hand crafted features and three related methods based on CNN for image steganalysis and/or forensics, achieving the state-of-the-art results. Furthermore, we provide more supplementary results to show the rationality and robustness of the proposed model.

Image Processing Operations Identification via Convolutional Neural Network

http://arxiv.org/pdf/1709.02908v1

Title Image Processing Operations Identification via Convolutional Neural Network Summary recent year image forensics attracted attention many forensic method proposed identifying image processing operation existing method based hand crafted feature one specific operation considered method many forensic scenario however multiple classification various image processing operation practical Besides difficult obtain effective feature hand image processing operation paper therefore propose new convolutional neural network CNN based method adaptively learn discriminative feature identifying typical image processing operation carefully design high pas filter bank get image residual input image channel expansion layer mix resulting residual pooling layer activation function employed method extensive result show proposed method outperform currently best method based hand crafted feature three related method based CNN image steganalysis andor forensics achieving stateoftheart result Furthermore provide supplementary result show rationality robustness proposed model 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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['Vahid Moosavi']

1709.02939v2

Study of urban form is an important area of research in urban planning/design that contributes to our understanding of how cities function and evolve. However, classical approaches are based on very limited observations and inconsistent methods. As an alternative, availability of massive urban data collections such as Open Street Map from the one hand and the recent advancements in machine learning methods such as deep learning techniques on the other have opened up new possibilities to automatically investigate urban forms at the global scale. In this work for the first time, by collecting a large data set of street networks in more than one million cities, towns and villages all over the world, we trained a deep convolutional auto-encoder, that automatically learns the hierarchical structures of urban forms and represents them via dense and comparable vectors. We showed how the learned urban vectors could be used for different investigations. Using the learned urban vectors, one is able to easily find and compare similar urban forms all over the world, considering their overall spatial structure and other factors such as orientation, graphical structure, and density and partial deformations. Further cluster analysis reveals the distribution of the main patterns of urban forms all over the planet.

Urban morphology meets deep learning: Exploring urban forms in one million cities, town and villages across the planet

http://arxiv.org/pdf/1709.02939v2

Title Urban morphology meet deep learning Exploring urban form one million city town village across planet Summary Study urban form important area research urban planningdesign contributes understanding city function evolve However classical approach based limited observation inconsistent method alternative availability massive urban data collection Open Street Map one hand recent advancement machine learning method deep learning technique opened new possibility automatically investigate urban form global scale work first time collecting large data set street network one million city town village world trained deep convolutional autoencoder automatically learns hierarchical structure urban form represents via dense comparable vector showed learned urban vector could used different investigation Using learned urban vector one able easily find compare similar urban form world considering overall spatial structure factor orientation graphical structure density partial deformation cluster analysis reveals distribution main pattern urban form planet 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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['Lee Clement', 'Jonathan Kelly']

1709.03009v3

Direct visual localization has recently enjoyed a resurgence in popularity with the increasing availability of cheap mobile computing power. The competitive accuracy and robustness of these algorithms compared to state-of-the-art feature-based methods, as well as their natural ability to yield dense maps, makes them an appealing choice for a variety of mobile robotics applications. However, direct methods remain brittle in the face of appearance change due to their underlying assumption of photometric consistency, which is commonly violated in practice. In this paper, we propose to mitigate this problem by training deep convolutional encoder-decoder models to transform images of a scene such that they correspond to a previously-seen canonical appearance. We validate our method in multiple environments and illumination conditions using high-fidelity synthetic RGB-D datasets, and integrate the trained models into a direct visual localization pipeline, yielding improvements in visual odometry (VO) accuracy through time-varying illumination conditions, as well as improved metric relocalization performance under illumination change, where conventional methods normally fail. We further provide a preliminary investigation of transfer learning from synthetic to real environments in a localization context. An open-source implementation of our method using PyTorch is available at https://github.com/utiasSTARS/cat-net.

How to Train a CAT: Learning Canonical Appearance Transformations for Direct Visual Localization Under Illumination Change

http://arxiv.org/pdf/1709.03009v3

Title Train CAT Learning Canonical Appearance Transformations Direct Visual Localization Illumination Change Summary Direct visual localization recently enjoyed resurgence popularity increasing availability cheap mobile computing power competitive accuracy robustness algorithm compared stateoftheart featurebased method well natural ability yield dense map make appealing choice variety mobile robotics application However direct method remain brittle face appearance change due underlying assumption photometric consistency commonly violated practice paper propose mitigate problem training deep convolutional encoderdecoder model transform image scene correspond previouslyseen canonical appearance validate method multiple environment illumination condition using highfidelity synthetic RGBD datasets integrate trained model direct visual localization pipeline yielding improvement visual odometry VO accuracy timevarying illumination condition well improved metric relocalization performance illumination change conventional method normally fail provide preliminary investigation transfer learning synthetic real environment localization context opensource implementation method using PyTorch available httpsgithubcomutiasSTARScatnet 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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['Mohammadhassan Izadyyazdanabadi', 'Evgenii Belykh', 'Michael Mooney', 'Nikolay Martirosyan', 'Jennifer Eschbacher', 'Peter Nakaji', 'Mark C. Preul', 'Yezhou Yang']

1709.03028v2

Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence technology undergoing assessment for applications in brain tumor surgery. Despite its promising potential, interpreting the unfamiliar gray tone images of fluorescent stains can be difficult. Many of the CLE images can be distorted by motion, extremely low or high fluorescence signal, or obscured by red blood cell accumulation, and these can be interpreted as nondiagnostic. However, just one neat CLE image might suffice for intraoperative diagnosis of the tumor. While manual examination of thousands of nondiagnostic images during surgery would be impractical, this creates an opportunity for a model to select diagnostic images for the pathologists or surgeon's review. In this study, we sought to develop a deep learning model to automatically detect the diagnostic images using a manually annotated dataset, and we employed a patient-based nested cross-validation approach to explore generalizability of the model. We explored various training regimes: deep training, shallow fine-tuning, and deep fine-tuning. Further, we investigated the effect of ensemble modeling by combining the top-5 single models crafted in the development phase. We localized histological features from diagnostic CLE images by visualization of shallow and deep neural activations. Our inter-rater experiment results confirmed that our ensemble of deeply fine-tuned models achieved higher agreement with the ground truth than the other observers. With the speed and precision of the proposed method (110 images/second; 85% on the gold standard test subset), it has potential to be integrated into the operative workflow in the brain tumor surgery.

Convolutional Neural Networks: Ensemble Modeling, Fine-Tuning and Unsupervised Semantic Localization for Intraoperative CLE Images

http://arxiv.org/pdf/1709.03028v2

Title Convolutional Neural Networks Ensemble Modeling FineTuning Unsupervised Semantic Localization Intraoperative CLE Images Summary Confocal laser endomicroscopy CLE advanced optical fluorescence technology undergoing assessment application brain tumor surgery Despite promising potential interpreting unfamiliar gray tone image fluorescent stain difficult Many CLE image distorted motion extremely low high fluorescence signal obscured red blood cell accumulation interpreted nondiagnostic However one neat CLE image might suffice intraoperative diagnosis tumor manual examination thousand nondiagnostic image surgery would impractical creates opportunity model select diagnostic image pathologist surgeon review study sought develop deep learning model automatically detect diagnostic image using manually annotated dataset employed patientbased nested crossvalidation approach explore generalizability model explored various training regime deep training shallow finetuning deep finetuning investigated effect ensemble modeling combining top5 single model crafted development phase localized histological feature diagnostic CLE image visualization shallow deep neural activation interrater experiment result confirmed ensemble deeply finetuned model achieved higher agreement ground truth observer speed precision proposed method 110 imagessecond 85 gold standard test subset potential integrated operative workflow brain tumor 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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['Inkyu Sa', 'Zetao Chen', 'Marija Popovic', 'Raghav Khanna', 'Frank Liebisch', 'Juan Nieto', 'Roland Siegwart']

1709.03329v1

Selective weed treatment is a critical step in autonomous crop management as related to crop health and yield. However, a key challenge is reliable, and accurate weed detection to minimize damage to surrounding plants. In this paper, we present an approach for dense semantic weed classification with multispectral images collected by a micro aerial vehicle (MAV). We use the recently developed encoder-decoder cascaded Convolutional Neural Network (CNN), Segnet, that infers dense semantic classes while allowing any number of input image channels and class balancing with our sugar beet and weed datasets. To obtain training datasets, we established an experimental field with varying herbicide levels resulting in field plots containing only either crop or weed, enabling us to use the Normalized Difference Vegetation Index (NDVI) as a distinguishable feature for automatic ground truth generation. We train 6 models with different numbers of input channels and condition (fine-tune) it to achieve about 0.8 F1-score and 0.78 Area Under the Curve (AUC) classification metrics. For model deployment, an embedded GPU system (Jetson TX2) is tested for MAV integration. Dataset used in this paper is released to support the community and future work.

weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming

http://arxiv.org/pdf/1709.03329v1

Title weedNet Dense Semantic Weed Classification Using Multispectral Images MAV Smart Farming Summary Selective weed treatment critical step autonomous crop management related crop health yield However key challenge reliable accurate weed detection minimize damage surrounding plant paper present approach dense semantic weed classification multispectral image collected micro aerial vehicle MAV use recently developed encoderdecoder cascaded Convolutional Neural Network CNN Segnet infers dense semantic class allowing number input image channel class balancing sugar beet weed datasets obtain training datasets established experimental field varying herbicide level resulting field plot containing either crop weed enabling u use Normalized Difference Vegetation Index NDVI distinguishable feature automatic ground truth generation train 6 model different number input channel condition finetune achieve 08 F1score 078 Area Curve AUC classification metric model deployment embedded GPU system Jetson TX2 tested MAV integration Dataset used paper released support community future work 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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['Yusuke Matsui', 'Keisuke Ogaki', 'Toshihiko Yamasaki', 'Kiyoharu Aizawa']

1709.03708v1

Data clustering is a fundamental operation in data analysis. For handling large-scale data, the standard k-means clustering method is not only slow, but also memory-inefficient. We propose an efficient clustering method for billion-scale feature vectors, called PQk-means. By first compressing input vectors into short product-quantized (PQ) codes, PQk-means achieves fast and memory-efficient clustering, even for high-dimensional vectors. Similar to k-means, PQk-means repeats the assignment and update steps, both of which can be performed in the PQ-code domain. Experimental results show that even short-length (32 bit) PQ-codes can produce competitive results compared with k-means. This result is of practical importance for clustering in memory-restricted environments. Using the proposed PQk-means scheme, the clustering of one billion 128D SIFT features with K = 10^5 is achieved within 14 hours, using just 32 GB of memory consumption on a single computer.

PQk-means: Billion-scale Clustering for Product-quantized Codes

http://arxiv.org/pdf/1709.03708v1

Title PQkmeans Billionscale Clustering Productquantized Codes Summary Data clustering fundamental operation data analysis handling largescale data standard kmeans clustering method slow also memoryinefficient propose efficient clustering method billionscale feature vector called PQkmeans first compressing input vector short productquantized PQ code PQkmeans achieves fast memoryefficient clustering even highdimensional vector Similar kmeans PQkmeans repeat assignment update step performed PQcode domain Experimental result show even shortlength 32 bit PQcodes produce competitive result compared kmeans result practical importance clustering memoryrestricted environment Using proposed PQkmeans scheme clustering one billion 128D SIFT feature K 105 achieved within 14 hour using 32 GB memory consumption single computer 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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['Ehsan Arbabi', 'Mohammad Shabani', 'Ali Yarigholi']

1709.03717v1

Human eye gaze detection plays an important role in various fields, including human-computer interaction, virtual reality and cognitive science. Although different relatively accurate systems of eye tracking and gaze detection exist, they are usually either too expensive to be bought for low cost applications or too complex to be implemented easily. In this article, we propose a non-wearable system for eye tracking and gaze detection with low complexity and cost. The proposed system provides a medium accuracy which makes it suitable for general applications in which low cost and easy implementation is more important than achieving very precise gaze detection. The proposed method includes pupil and marker detection using infrared image processing, and gaze evaluation using an interpolation-based strategy. The interpolation-based strategy exploits the positions of the detected pupils and markers in a target captured image and also in some previously captured training images for estimating the position of a point that the user is gazing at. The proposed system has been evaluated by three users in two different lighting conditions. The experimental results show that the accuracy of this low cost system can be between 90% and 100% for finding major gazing directions.

A low cost non-wearable gaze detection system based on infrared image processing

http://arxiv.org/pdf/1709.03717v1

Title low cost nonwearable gaze detection system based infrared image processing Summary Human eye gaze detection play important role various field including humancomputer interaction virtual reality cognitive science Although different relatively accurate system eye tracking gaze detection exist usually either expensive bought low cost application complex implemented easily article propose nonwearable system eye tracking gaze detection low complexity cost proposed system provides medium accuracy make suitable general application low cost easy implementation important achieving precise gaze detection proposed method includes pupil marker detection using infrared image processing gaze evaluation using interpolationbased strategy interpolationbased strategy exploit position detected pupil marker target captured image also previously captured training image estimating position point user gazing proposed system evaluated three user two different lighting condition experimental result show accuracy low cost system 90 100 finding major gazing 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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['Jens Grubert', 'Yuta Itoh', 'Kenneth Moser', 'J. Edward Swan II']

1709.04299v1

Optical see-through head-mounted displays (OST HMDs) are a major output medium for Augmented Reality, which have seen significant growth in popularity and usage among the general public due to the growing release of consumer-oriented models, such as the Microsoft Hololens. Unlike Virtual Reality headsets, OST HMDs inherently support the addition of computer-generated graphics directly into the light path between a user's eyes and their view of the physical world. As with most Augmented and Virtual Reality systems, the physical position of an OST HMD is typically determined by an external or embedded 6-Degree-of-Freedom tracking system. However, in order to properly render virtual objects, which are perceived as spatially aligned with the physical environment, it is also necessary to accurately measure the position of the user's eyes within the tracking system's coordinate frame. For over 20 years, researchers have proposed various calibration methods to determine this needed eye position. However, to date, there has not been a comprehensive overview of these procedures and their requirements. Hence, this paper surveys the field of calibration methods for OST HMDs. Specifically, it provides insights into the fundamentals of calibration techniques, and presents an overview of both manual and automatic approaches, as well as evaluation methods and metrics. Finally, it also identifies opportunities for future research. % relative to the tracking coordinate system, and, hence, its position in 3D space.

A Survey of Calibration Methods for Optical See-Through Head-Mounted Displays

http://arxiv.org/pdf/1709.04299v1

Title Survey Calibration Methods Optical SeeThrough HeadMounted Displays Summary Optical seethrough headmounted display OST HMDs major output medium Augmented Reality seen significant growth popularity usage among general public due growing release consumeroriented model Microsoft Hololens Unlike Virtual Reality headset OST HMDs inherently support addition computergenerated graphic directly light path user eye view physical world Augmented Virtual Reality system physical position OST HMD typically determined external embedded 6DegreeofFreedom tracking system However order properly render virtual object perceived spatially aligned physical environment also necessary accurately measure position user eye within tracking system coordinate frame 20 year researcher proposed various calibration method determine needed eye position However date comprehensive overview procedure requirement Hence paper survey field calibration method OST HMDs Specifically provides insight fundamental calibration technique present overview manual automatic approach well evaluation method metric Finally also identifies opportunity future research relative tracking coordinate system hence position 3D space 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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['Keunwoo Choi', 'György Fazekas', 'Kyunghyun Cho', 'Mark Sandler']

1709.04396v1

Following their success in Computer Vision and other areas, deep learning techniques have recently become widely adopted in Music Information Retrieval (MIR) research. However, the majority of works aim to adopt and assess methods that have been shown to be effective in other domains, while there is still a great need for more original research focusing on music primarily and utilising musical knowledge and insight. The goal of this paper is to boost the interest of beginners by providing a comprehensive tutorial and reducing the barriers to entry into deep learning for MIR. We lay out the basic principles and review prominent works in this hard to navigate field. We then outline the network structures that have been successful in MIR problems and facilitate the selection of building blocks for the problems at hand. Finally, guidelines for new tasks and some advanced topics in deep learning are discussed to stimulate new research in this fascinating field.

A Tutorial on Deep Learning for Music Information Retrieval

http://arxiv.org/pdf/1709.04396v1

Title Tutorial Deep Learning Music Information Retrieval Summary Following success Computer Vision area deep learning technique recently become widely adopted Music Information Retrieval MIR research However majority work aim adopt ass method shown effective domain still great need original research focusing music primarily utilising musical knowledge insight goal paper boost interest beginner providing comprehensive tutorial reducing barrier entry deep learning MIR lay basic principle review prominent work hard navigate field outline network structure successful MIR problem facilitate selection building block problem hand Finally guideline new task advanced topic deep learning discussed stimulate new research fascinating field 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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['Gang Cao', 'Lihui Huang', 'Huawei Tian', 'Xianglin Huang', 'Yongbin Wang', 'Ruicong Zhi']

1709.04427v1

As an efficient image contrast enhancement (CE) tool, adaptive gamma correction (AGC) was previously proposed by relating gamma parameter with cumulative distribution function (CDF) of the pixel gray levels within an image. ACG deals well with most dimmed images, but fails for globally bright images and the dimmed images with local bright regions. Such two categories of brightness-distorted images are universal in real scenarios, such as improper exposure and white object regions. In order to attenuate such deficiencies, here we propose an improved AGC algorithm. The novel strategy of negative images is used to realize CE of the bright images, and the gamma correction modulated by truncated CDF is employed to enhance the dimmed ones. As such, local over-enhancement and structure distortion can be alleviated. Both qualitative and quantitative experimental results show that our proposed method yields consistently good CE results.

Contrast Enhancement of Brightness-Distorted Images by Improved Adaptive Gamma Correction

http://arxiv.org/pdf/1709.04427v1

Title Contrast Enhancement BrightnessDistorted Images Improved Adaptive Gamma Correction Summary efficient image contrast enhancement CE tool adaptive gamma correction AGC previously proposed relating gamma parameter cumulative distribution function CDF pixel gray level within image ACG deal well dimmed image fails globally bright image dimmed image local bright region two category brightnessdistorted image universal real scenario improper exposure white object region order attenuate deficiency propose improved AGC algorithm novel strategy negative image used realize CE bright image gamma correction modulated truncated CDF employed enhance dimmed one local overenhancement structure distortion alleviated qualitative quantitative experimental result show proposed method yield consistently good CE result 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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['Jinhui Yu', 'Kailin Wu', 'Kang Zhang', 'Xianjun Sam Zheng']

1709.04550v1

Negative afterimage appears in our vision when we shift our gaze from an over stimulated original image to a new area with a uniform color. The colors of negative afterimages differ from the old stimulating colors in the original image when the color in the new area is either neutral or chromatic. The interaction between stimulating colors in the test and inducing field in the original image changes our color perception due to simultaneous contrast, and the interaction between changed colors perceived in the previously-viewed field and the color in the currently-viewed field also affects our perception of colors in negative afterimages due to successive contrast. Based on these observations we propose a computational model to estimate colors of negative afterimages in more general cases where the original stimulating color in the test field is chromatic, and the original stimulating color in the inducing field and the new stimulating color can be either neutral or chromatic. We validate our model with human experiments.

A Computational Model of Afterimages based on Simultaneous and Successive Contrasts

http://arxiv.org/pdf/1709.04550v1

Title Computational Model Afterimages based Simultaneous Successive Contrasts Summary Negative afterimage appears vision shift gaze stimulated original image new area uniform color color negative afterimage differ old stimulating color original image color new area either neutral chromatic interaction stimulating color test inducing field original image change color perception due simultaneous contrast interaction changed color perceived previouslyviewed field color currentlyviewed field also affect perception color negative afterimage due successive contrast Based observation propose computational model estimate color negative afterimage general case original stimulating color test field chromatic original stimulating color inducing field new stimulating color either neutral chromatic validate model human 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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['Gang Cao', 'Huawei Tian', 'Lifang Yu', 'Xianglin Huang', 'Yongbin Wang']

1709.04583v2

In this paper, we propose a general framework to accelerate the universal histogram-based image contrast enhancement (CE) algorithms. Both spatial and gray-level selective down- sampling of digital images are adopted to decrease computational cost, while the visual quality of enhanced images is still preserved and without apparent degradation. Mapping function calibration is novelly proposed to reconstruct the pixel mapping on the gray levels missed by downsampling. As two case studies, accelerations of histogram equalization (HE) and the state-of-the-art global CE algorithm, i.e., spatial mutual information and PageRank (SMIRANK), are presented detailedly. Both quantitative and qualitative assessment results have verified the effectiveness of our proposed CE acceleration framework. In typical tests, computational efficiencies of HE and SMIRANK have been speeded up by about 3.9 and 13.5 times, respectively.

Acceleration of Histogram-Based Contrast Enhancement via Selective Downsampling

http://arxiv.org/pdf/1709.04583v2

Title Acceleration HistogramBased Contrast Enhancement via Selective Downsampling Summary paper propose general framework accelerate universal histogrambased image contrast enhancement CE algorithm spatial graylevel selective sampling digital image adopted decrease computational cost visual quality enhanced image still preserved without apparent degradation Mapping function calibration novelly proposed reconstruct pixel mapping gray level missed downsampling two case study acceleration histogram equalization stateoftheart global CE algorithm ie spatial mutual information PageRank SMIRANK presented detailedly quantitative qualitative assessment result verified effectiveness proposed CE acceleration framework typical test computational efficiency SMIRANK speeded 39 135 time respectively 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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['Yair Meidan', 'Michael Bohadana', 'Asaf Shabtai', 'Martin Ochoa', 'Nils Ole Tippenhauer', 'Juan Davis Guarnizo', 'Yuval Elovici']

1709.04647v1

Security experts have demonstrated numerous risks imposed by Internet of Things (IoT) devices on organizations. Due to the widespread adoption of such devices, their diversity, standardization obstacles, and inherent mobility, organizations require an intelligent mechanism capable of automatically detecting suspicious IoT devices connected to their networks. In particular, devices not included in a white list of trustworthy IoT device types (allowed to be used within the organizational premises) should be detected. In this research, Random Forest, a supervised machine learning algorithm, was applied to features extracted from network traffic data with the aim of accurately identifying IoT device types from the white list. To train and evaluate multi-class classifiers, we collected and manually labeled network traffic data from 17 distinct IoT devices, representing nine types of IoT devices. Based on the classification of 20 consecutive sessions and the use of majority rule, IoT device types that are not on the white list were correctly detected as unknown in 96% of test cases (on average), and white listed device types were correctly classified by their actual types in 99% of cases. Some IoT device types were identified quicker than others (e.g., sockets and thermostats were successfully detected within five TCP sessions of connecting to the network). Perfect detection of unauthorized IoT device types was achieved upon analyzing 110 consecutive sessions; perfect classification of white listed types required 346 consecutive sessions, 110 of which resulted in 99.49% accuracy. Further experiments demonstrated the successful applicability of classifiers trained in one location and tested on another. In addition, a discussion is provided regarding the resilience of our machine learning-based IoT white listing method to adversarial attacks.

Detection of Unauthorized IoT Devices Using Machine Learning Techniques

http://arxiv.org/pdf/1709.04647v1

Title Detection Unauthorized IoT Devices Using Machine Learning Techniques Summary Security expert demonstrated numerous risk imposed Internet Things IoT device organization Due widespread adoption device diversity standardization obstacle inherent mobility organization require intelligent mechanism capable automatically detecting suspicious IoT device connected network particular device included white list trustworthy IoT device type allowed used within organizational premise detected research Random Forest supervised machine learning algorithm applied feature extracted network traffic data aim accurately identifying IoT device type white list train evaluate multiclass classifier collected manually labeled network traffic data 17 distinct IoT device representing nine type IoT device Based classification 20 consecutive session use majority rule IoT device type white list correctly detected unknown 96 test case average white listed device type correctly classified actual type 99 case IoT device type identified quicker others eg socket thermostat successfully detected within five TCP session connecting network Perfect detection unauthorized IoT device type achieved upon analyzing 110 consecutive session perfect classification white listed type required 346 consecutive session 110 resulted 9949 accuracy experiment demonstrated successful applicability classifier trained one location tested another addition discussion provided regarding resilience machine learningbased IoT white listing method adversarial attack 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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['Tomas Werner']

1709.04989v1

A popular class of algorithms to optimize the dual LP relaxation of the discrete energy minimization problem (a.k.a.\ MAP inference in graphical models or valued constraint satisfaction) are convergent message-passing algorithms, such as max-sum diffusion, TRW-S, MPLP and SRMP. These algorithms are successful in practice, despite the fact that they are a version of coordinate minimization applied to a convex piecewise-affine function, which is not guaranteed to converge to a global minimizer. These algorithms converge only to a local minimizer, characterized by local consistency known from constraint programming. We generalize max-sum diffusion to a version of coordinate minimization applicable to an arbitrary convex piecewise-affine function, which converges to a local consistency condition. This condition can be seen as the sign relaxation of the global optimality condition.

On Coordinate Minimization of Convex Piecewise-Affine Functions

http://arxiv.org/pdf/1709.04989v1

Title Coordinate Minimization Convex PiecewiseAffine Functions Summary popular class algorithm optimize dual LP relaxation discrete energy minimization problem aka MAP inference graphical model valued constraint satisfaction convergent messagepassing algorithm maxsum diffusion TRWS MPLP SRMP algorithm successful practice despite fact version coordinate minimization applied convex piecewiseaffine function guaranteed converge global minimizer algorithm converge local minimizer characterized local consistency known constraint programming generalize maxsum diffusion version coordinate minimization applicable arbitrary convex piecewiseaffine function converges local consistency condition condition seen sign relaxation global optimality condition 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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['Frank Ong', 'Joseph Cheng', 'Michael Lustig']

1709.05374v1

Purpose: To develop a general phase regularized image reconstruction method, with applications to partial Fourier imaging, water-fat imaging and flow imaging. Theory and Methods: The problem of enforcing phase constraints in reconstruction was studied under a regularized inverse problem framework. A general phase regularized reconstruction algorithm was proposed to enable various joint reconstruction of partial Fourier imaging, water-fat imaging and flow imaging, along with parallel imaging (PI) and compressed sensing (CS). Since phase regularized reconstruction is inherently non-convex and sensitive to phase wraps in the initial solution, a reconstruction technique, named phase cycling, was proposed to render the overall algorithm invariant to phase wraps. The proposed method was applied to retrospectively under-sampled in vivo datasets and compared with state of the art reconstruction methods. Results: Phase cycling reconstructions showed reduction of artifacts compared to reconstructions with- out phase cycling and achieved similar performances as state of the art results in partial Fourier, water-fat and divergence-free regularized flow reconstruction. Joint reconstruction of partial Fourier + water-fat imaging + PI + CS, and partial Fourier + divergence-free regularized flow imaging + PI + CS were demonstrated. Conclusion: The proposed phase cycling reconstruction provides an alternative way to perform phase regularized reconstruction, without the need to perform phase unwrapping. It is robust to the choice of initial solutions and encourages the joint reconstruction of phase imaging applications.

General Phase Regularized Reconstruction using Phase Cycling

http://arxiv.org/pdf/1709.05374v1

Title General Phase Regularized Reconstruction using Phase Cycling Summary Purpose develop general phase regularized image reconstruction method application partial Fourier imaging waterfat imaging flow imaging Theory Methods problem enforcing phase constraint reconstruction studied regularized inverse problem framework general phase regularized reconstruction algorithm proposed enable various joint reconstruction partial Fourier imaging waterfat imaging flow imaging along parallel imaging PI compressed sensing CS Since phase regularized reconstruction inherently nonconvex sensitive phase wrap initial solution reconstruction technique named phase cycling proposed render overall algorithm invariant phase wrap proposed method applied retrospectively undersampled vivo datasets compared state art reconstruction method Results Phase cycling reconstruction showed reduction artifact compared reconstruction phase cycling achieved similar performance state art result partial Fourier waterfat divergencefree regularized flow reconstruction Joint reconstruction partial Fourier waterfat imaging PI CS partial Fourier divergencefree regularized flow imaging PI CS demonstrated Conclusion proposed phase cycling reconstruction provides alternative way perform phase regularized reconstruction without need perform phase unwrapping robust choice initial solution encourages joint reconstruction phase imaging 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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['Noriaki Hirose', 'Amir Sadeghian', 'Patrick Goebel', 'Silvio Savarese']

1709.05439v1

It is important for robots to be able to decide whether they can go through a space or not, as they navigate through a dynamic environment. This capability can help them avoid injury or serious damage, e.g., as a result of running into people and obstacles, getting stuck, or falling off an edge. To this end, we propose an unsupervised and a near-unsupervised method based on Generative Adversarial Networks (GAN) to classify scenarios as traversable or not based on visual data. Our method is inspired by the recent success of data-driven approaches on computer vision problems and anomaly detection, and reduces the need for vast amounts of negative examples at training time. Collecting negative data indicating that a robot should not go through a space is typically hard and dangerous because of collisions, whereas collecting positive data can be automated and done safely based on the robot's own traveling experience. We verify the generality and effectiveness of the proposed approach on a test dataset collected in a previously unseen environment with a mobile robot. Furthermore, we show that our method can be used to build costmaps (we call as "GoNoGo" costmaps) for robot path planning using visual data only.

To Go or Not To Go? A Near Unsupervised Learning Approach For Robot Navigation

http://arxiv.org/pdf/1709.05439v1

Title Go Go Near Unsupervised Learning Approach Robot Navigation Summary important robot able decide whether go space navigate dynamic environment capability help avoid injury serious damage eg result running people obstacle getting stuck falling edge end propose unsupervised nearunsupervised method based Generative Adversarial Networks GAN classify scenario traversable based visual data method inspired recent success datadriven approach computer vision problem anomaly detection reduces need vast amount negative example training time Collecting negative data indicating robot go space typically hard dangerous collision whereas collecting positive data automated done safely based robot traveling experience verify generality effectiveness proposed approach test dataset collected previously unseen environment mobile robot Furthermore show method used build costmaps call GoNoGo costmaps robot path planning using visual data 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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['Thomas Probst', 'Kevis-Kokitsi Maninis', 'Ajad Chhatkuli', 'Mouloud Ourak', 'Emmanuel Vander Poorten', 'Luc Van Gool']

1709.05665v2

Computer vision and robotics are being increasingly applied in medical interventions. Especially in interventions where extreme precision is required they could make a difference. One such application is robot-assisted retinal microsurgery. In recent works, such interventions are conducted under a stereo-microscope, and with a robot-controlled surgical tool. The complementarity of computer vision and robotics has however not yet been fully exploited. In order to improve the robot control we are interested in 3D reconstruction of the anatomy and in automatic tool localization using a stereo microscope. In this paper, we solve this problem for the first time using a single pipeline, starting from uncalibrated cameras to reach metric 3D reconstruction and registration, in retinal microsurgery. The key ingredients of our method are: (a) surgical tool landmark detection, and (b) 3D reconstruction with the stereo microscope, using the detected landmarks. To address the former, we propose a novel deep learning method that detects and recognizes keypoints in high definition images at higher than real-time speed. We use the detected 2D keypoints along with their corresponding 3D coordinates obtained from the robot sensors to calibrate the stereo microscope using an affine projection model. We design an online 3D reconstruction pipeline that makes use of smoothness constraints and performs robot-to-camera registration. The entire pipeline is extensively validated on open-sky porcine eye sequences. Quantitative and qualitative results are presented for all steps.

Automatic Tool Landmark Detection for Stereo Vision in Robot-Assisted Retinal Surgery

http://arxiv.org/pdf/1709.05665v2

Title Automatic Tool Landmark Detection Stereo Vision RobotAssisted Retinal Surgery Summary Computer vision robotics increasingly applied medical intervention Especially intervention extreme precision required could make difference One application robotassisted retinal microsurgery recent work intervention conducted stereomicroscope robotcontrolled surgical tool complementarity computer vision robotics however yet fully exploited order improve robot control interested 3D reconstruction anatomy automatic tool localization using stereo microscope paper solve problem first time using single pipeline starting uncalibrated camera reach metric 3D reconstruction registration retinal microsurgery key ingredient method surgical tool landmark detection b 3D reconstruction stereo microscope using detected landmark address former propose novel deep learning method detects recognizes keypoints high definition image higher realtime speed use detected 2D keypoints along corresponding 3D coordinate obtained robot sensor calibrate stereo microscope using affine projection model design online 3D reconstruction pipeline make use smoothness constraint performs robottocamera registration entire pipeline extensively validated opensky porcine eye sequence Quantitative qualitative result presented step 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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['Mohammad Reza Loghmani', 'Barbara Caputo', 'Markus Vincze']

1709.05862v1

The ability to recognize objects is an essential skill for a robotic system acting in human-populated environments. Despite decades of effort from the robotic and vision research communities, robots are still missing good visual perceptual systems, preventing the use of autonomous agents for real-world applications. The progress is slowed down by the lack of a testbed able to accurately represent the world perceived by the robot in-the-wild. In order to fill this gap, we introduce a large-scale, multi-view object dataset collected with an RGB-D camera mounted on a mobile robot. The dataset embeds the challenges faced by a robot in a real-life application and provides a useful tool for validating object recognition algorithms. Besides describing the characteristics of the dataset, the paper evaluates the performance of a collection of well-established deep convolutional networks on the new dataset and analyzes the transferability of deep representations from Web images to robotic data. Despite the promising results obtained with such representations, the experiments demonstrate that object classification with real-life robotic data is far from being solved. Finally, we provide a comparative study to analyze and highlight the open challenges in robot vision, explaining the discrepancies in the performance.

Recognizing Objects In-the-wild: Where Do We Stand?

http://arxiv.org/pdf/1709.05862v1

Title Recognizing Objects Inthewild Stand Summary ability recognize object essential skill robotic system acting humanpopulated environment Despite decade effort robotic vision research community robot still missing good visual perceptual system preventing use autonomous agent realworld application progress slowed lack testbed able accurately represent world perceived robot inthewild order fill gap introduce largescale multiview object dataset collected RGBD camera mounted mobile robot dataset embeds challenge faced robot reallife application provides useful tool validating object recognition algorithm Besides describing characteristic dataset paper evaluates performance collection wellestablished deep convolutional network new dataset analyzes transferability deep representation Web image robotic data Despite promising result obtained representation experiment demonstrate object classification reallife robotic data far solved Finally provide comparative study analyze highlight open challenge robot vision explaining discrepancy 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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['Christoph Reinders', 'Hanno Ackermann', 'Michael Ying Yang', 'Bodo Rosenhahn']

1709.05910v3

In recent years, data-driven methods have shown great success for extracting information about the infrastruc- ture in urban areas. These algorithms are usually trained on large datasets consisting of thousands or millions of labeled training examples. While large datasets have been published regarding cars, for cyclists very few labeled data is available although appearance, point of view, and positioning of even relevant objects differ. Unfortunately, labeling data is costly and requires a huge amount of work. In this paper, we thus address the problem of learning with very few labels. The aim is to recognize particular traffic signs in crowdsourced data to collect information which is of interest to cyclists. We propose a system for object recognition that is trained with only 15 examples per class on average. To achieve this, we combine the advantages of convolutional neural networks and random forests to learn a patch-wise classifier. In the next step, we map the random forest to a neural network and transform the classifier to a fully convolutional network. Thereby, the processing of full images is significantly accelerated and bounding boxes can be predicted. Finally, we integrate data of the Global Positioning System (GPS) to localize the predictions on the map. In comparison to Faster R-CNN and other networks for object recognition or algorithms for transfer learning, we considerably reduce the required amount of labeled data. We demonstrate good performance on the recognition of traffic signs for cyclists as well as their localization in maps.

Learning a Fully Convolutional Network for Object Recognition using very few Data

http://arxiv.org/pdf/1709.05910v3

Title Learning Fully Convolutional Network Object Recognition using Data Summary recent year datadriven method shown great success extracting information infrastruc ture urban area algorithm usually trained large datasets consisting thousand million labeled training example large datasets published regarding car cyclist labeled data available although appearance point view positioning even relevant object differ Unfortunately labeling data costly requires huge amount work paper thus address problem learning label aim recognize particular traffic sign crowdsourced data collect information interest cyclist propose system object recognition trained 15 example per class average achieve combine advantage convolutional neural network random forest learn patchwise classifier next step map random forest neural network transform classifier fully convolutional network Thereby processing full image significantly accelerated bounding box predicted Finally integrate data Global Positioning System GPS localize prediction map comparison Faster RCNN network object recognition algorithm transfer learning considerably reduce required amount labeled data demonstrate good performance recognition traffic sign cyclist well localization map 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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['Yuan Liu', 'Stéphane Canu', 'Paul Honeine', 'Su Ruan']

1709.05937v1

Sparse representation learning has recently gained a great success in signal and image processing, thanks to recent advances in dictionary learning. To this end, the $\ell_0$-norm is often used to control the sparsity level. Nevertheless, optimization problems based on the $\ell_0$-norm are non-convex and NP-hard. For these reasons, relaxation techniques have been attracting much attention of researchers, by priorly targeting approximation solutions (e.g. $\ell_1$-norm, pursuit strategies). On the contrary, this paper considers the exact $\ell_0$-norm optimization problem and proves that it can be solved effectively, despite of its complexity. The proposed method reformulates the problem as a Mixed-Integer Quadratic Program (MIQP) and gets the global optimal solution by applying existing optimization software. Because the main difficulty of this approach is its computational time, two techniques are introduced that improve the computational speed. Finally, our method is applied to image denoising which shows its feasibility and relevance compared to the state-of-the-art.

Une véritable approche $\ell_0$ pour l'apprentissage de dictionnaire

http://arxiv.org/pdf/1709.05937v1

Title Une véritable approche ell0 pour lapprentissage de dictionnaire Summary Sparse representation learning recently gained great success signal image processing thanks recent advance dictionary learning end ell0norm often used control sparsity level Nevertheless optimization problem based ell0norm nonconvex NPhard reason relaxation technique attracting much attention researcher priorly targeting approximation solution eg ell1norm pursuit strategy contrary paper considers exact ell0norm optimization problem prof solved effectively despite complexity proposed method reformulates problem MixedInteger Quadratic Program MIQP get global optimal solution applying existing optimization software main difficulty approach computational time two technique introduced improve computational speed Finally method applied image denoising show feasibility relevance compared stateoftheart 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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['Nancy Xin Ru Wang', 'Ali Farhadi', 'Rajesh Rao', 'Bingni Brunton']

1709.05939v2

Developing useful interfaces between brains and machines is a grand challenge of neuroengineering. An effective interface has the capacity to not only interpret neural signals, but predict the intentions of the human to perform an action in the near future; prediction is made even more challenging outside well-controlled laboratory experiments. This paper describes our approach to detect and to predict natural human arm movements in the future, a key challenge in brain computer interfacing that has never before been attempted. We introduce the novel Annotated Joints in Long-term ECoG (AJILE) dataset; AJILE includes automatically annotated poses of 7 upper body joints for four human subjects over 670 total hours (more than 72 million frames), along with the corresponding simultaneously acquired intracranial neural recordings. The size and scope of AJILE greatly exceeds all previous datasets with movements and electrocorticography (ECoG), making it possible to take a deep learning approach to movement prediction. We propose a multimodal model that combines deep convolutional neural networks (CNN) with long short-term memory (LSTM) blocks, leveraging both ECoG and video modalities. We demonstrate that our models are able to detect movements and predict future movements up to 800 msec before movement initiation. Further, our multimodal movement prediction models exhibit resilience to simulated ablation of input neural signals. We believe a multimodal approach to natural neural decoding that takes context into account is critical in advancing bioelectronic technologies and human neuroscience.

AJILE Movement Prediction: Multimodal Deep Learning for Natural Human Neural Recordings and Video

http://arxiv.org/pdf/1709.05939v2

Title AJILE Movement Prediction Multimodal Deep Learning Natural Human Neural Recordings Video Summary Developing useful interface brain machine grand challenge neuroengineering effective interface capacity interpret neural signal predict intention human perform action near future prediction made even challenging outside wellcontrolled laboratory experiment paper describes approach detect predict natural human arm movement future key challenge brain computer interfacing never attempted introduce novel Annotated Joints Longterm ECoG AJILE dataset AJILE includes automatically annotated pose 7 upper body joint four human subject 670 total hour 72 million frame along corresponding simultaneously acquired intracranial neural recording size scope AJILE greatly exceeds previous datasets movement electrocorticography ECoG making possible take deep learning approach movement prediction propose multimodal model combine deep convolutional neural network CNN long shortterm memory LSTM block leveraging ECoG video modality demonstrate model able detect movement predict future movement 800 msec movement initiation multimodal movement prediction model exhibit resilience simulated ablation input neural signal believe multimodal approach natural neural decoding take context account critical advancing bioelectronic technology human neuroscience 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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['Bartolomeo Della Corte', 'Igor Bogoslavskyi', 'Cyrill Stachniss', 'Giorgio Grisetti']

1709.05945v1

The ability to build maps is a key functionality for the majority of mobile robots. A central ingredient to most mapping systems is the registration or alignment of the recorded sensor data. In this paper, we present a general methodology for photometric registration that can deal with multiple different cues. We provide examples for registering RGBD as well as 3D LIDAR data. In contrast to popular point cloud registration approaches such as ICP our method does not rely on explicit data association and exploits multiple modalities such as raw range and image data streams. Color, depth, and normal information are handled in an uniform manner and the registration is obtained by minimizing the pixel-wise difference between two multi-channel images. We developed a flexible and general framework and implemented our approach inside that framework. We also released our implementation as open source C++ code. The experiments show that our approach allows for an accurate registration of the sensor data without requiring an explicit data association or model-specific adaptations to datasets or sensors. Our approach exploits the different cues in a natural and consistent way and the registration can be done at framerate for a typical range or imaging sensor.

A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration

http://arxiv.org/pdf/1709.05945v1

Title General Framework Flexible MultiCue Photometric Point Cloud Registration Summary ability build map key functionality majority mobile robot central ingredient mapping system registration alignment recorded sensor data paper present general methodology photometric registration deal multiple different cue provide example registering RGBD well 3D LIDAR data contrast popular point cloud registration approach ICP method rely explicit data association exploit multiple modality raw range image data stream Color depth normal information handled uniform manner registration obtained minimizing pixelwise difference two multichannel image developed flexible general framework implemented approach inside framework also released implementation open source C code experiment show approach allows accurate registration sensor data without requiring explicit data association modelspecific adaptation datasets sensor approach exploit different cue natural consistent way registration done framerate typical range imaging 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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['Luis Contreras', 'Walterio Mayol-Cuevas']

1709.05972v1

This paper presents a study on the use of Convolutional Neural Networks for camera relocalisation and its application to map compression. We follow state of the art visual relocalisation results and evaluate response to different data inputs -- namely, depth, grayscale, RGB, spatial position and combinations of these. We use a CNN map representation and introduce the notion of map compression under this paradigm by using smaller CNN architectures without sacrificing relocalisation performance. We evaluate our proposal in a series of publicly available datasets over a number of CNN architectures with different sizes, both in complexity and number of layers. This formulation allows us to improve relocalisation accuracy by increasing the number of training trajectories while maintaining a constant-size CNN.

Towards CNN map representation and compression for camera relocalisation

http://arxiv.org/pdf/1709.05972v1

Title Towards CNN map representation compression camera relocalisation Summary paper present study use Convolutional Neural Networks camera relocalisation application map compression follow state art visual relocalisation result evaluate response different data input namely depth grayscale RGB spatial position combination use CNN map representation introduce notion map compression paradigm using smaller CNN architecture without sacrificing relocalisation performance evaluate proposal series publicly available datasets number CNN architecture different size complexity number layer formulation allows u improve relocalisation accuracy increasing number training trajectory maintaining constantsize 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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['Gabriele Costante', 'Thomas A. Ciarfuglia']

1709.06019v2

This work proposes a novel deep network architecture to solve the camera Ego-Motion estimation problem. A motion estimation network generally learns features similar to Optical Flow (OF) fields starting from sequences of images. This OF can be described by a lower dimensional latent space. Previous research has shown how to find linear approximations of this space. We propose to use an Auto-Encoder network to find a non-linear representation of the OF manifold. In addition, we propose to learn the latent space jointly with the estimation task, so that the learned OF features become a more robust description of the OF input. We call this novel architecture LS-VO. The experiments show that LS-VO achieves a considerable increase in performances in respect to baselines, while the number of parameters of the estimation network only slightly increases.

LS-VO: Learning Dense Optical Subspace for Robust Visual Odometry Estimation

http://arxiv.org/pdf/1709.06019v2

Title LSVO Learning Dense Optical Subspace Robust Visual Odometry Estimation Summary work proposes novel deep network architecture solve camera EgoMotion estimation problem motion estimation network generally learns feature similar Optical Flow field starting sequence image described lower dimensional latent space Previous research shown find linear approximation space propose use AutoEncoder network find nonlinear representation manifold addition propose learn latent space jointly estimation task learned feature become robust description input call novel architecture LSVO experiment show LSVO achieves considerable increase performance respect baseline number parameter estimation network slightly increase 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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['Itay Benou', 'Ronel Veksler', 'Alon Friedman', 'Tammy Riklin Raviv']

1709.06122v1

We present the concept of fiber-flux density for locally quantifying white matter (WM) fiber bundles. By combining scalar diffusivity measures (e.g., fractional anisotropy) with fiber-flux measurements, we define new local descriptors called Fiber-Flux Diffusion Density (FFDD) vectors. Applying each descriptor throughout fiber bundles allows along-tract coupling of a specific diffusion measure with geometrical properties, such as fiber orientation and coherence. A key step in the proposed framework is the construction of an FFDD dissimilarity measure for sub-voxel alignment of fiber bundles, based on the fast marching method (FMM). The obtained aligned WM tract-profiles enable meaningful inter-subject comparisons and group-wise statistical analysis. We demonstrate our method using two different datasets of contact sports players. Along-tract pairwise comparison as well as group-wise analysis, with respect to non-player healthy controls, reveal significant and spatially-consistent FFDD anomalies. Comparing our method with along-tract FA analysis shows improved sensitivity to subtle structural anomalies in football players over standard FA measurements.

Fiber-Flux Diffusion Density for White Matter Tracts Analysis: Application to Mild Anomalies Localization in Contact Sports Players

http://arxiv.org/pdf/1709.06122v1

Title FiberFlux Diffusion Density White Matter Tracts Analysis Application Mild Anomalies Localization Contact Sports Players Summary present concept fiberflux density locally quantifying white matter WM fiber bundle combining scalar diffusivity measure eg fractional anisotropy fiberflux measurement define new local descriptor called FiberFlux Diffusion Density FFDD vector Applying descriptor throughout fiber bundle allows alongtract coupling specific diffusion measure geometrical property fiber orientation coherence key step proposed framework construction FFDD dissimilarity measure subvoxel alignment fiber bundle based fast marching method FMM obtained aligned WM tractprofiles enable meaningful intersubject comparison groupwise statistical analysis demonstrate method using two different datasets contact sport player Alongtract pairwise comparison well groupwise analysis respect nonplayer healthy control reveal significant spatiallyconsistent FFDD anomaly Comparing method alongtract FA analysis show improved sensitivity subtle structural anomaly football player standard FA measurement 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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['Ashish Mahabal', 'Kshiteej Sheth', 'Fabian Gieseke', 'Akshay Pai', 'S. George Djorgovski', 'Andrew Drake', 'Matthew Graham', 'the CSS/CRTS/PTF Collaboration']

1709.06257v1

Astronomy light curves are sparse, gappy, and heteroscedastic. As a result standard time series methods regularly used for financial and similar datasets are of little help and astronomers are usually left to their own instruments and techniques to classify light curves. A common approach is to derive statistical features from the time series and to use machine learning methods, generally supervised, to separate objects into a few of the standard classes. In this work, we transform the time series to two-dimensional light curve representations in order to classify them using modern deep learning techniques. In particular, we show that convolutional neural networks based classifiers work well for broad characterization and classification. We use labeled datasets of periodic variables from CRTS survey and show how this opens doors for a quick classification of diverse classes with several possible exciting extensions.

Deep-Learnt Classification of Light Curves

http://arxiv.org/pdf/1709.06257v1

Title DeepLearnt Classification Light Curves Summary Astronomy light curve sparse gappy heteroscedastic result standard time series method regularly used financial similar datasets little help astronomer usually left instrument technique classify light curve common approach derive statistical feature time series use machine learning method generally supervised separate object standard class work transform time series twodimensional light curve representation order classify using modern deep learning technique particular show convolutional neural network based classifier work well broad characterization classification use labeled datasets periodic variable CRTS survey show open door quick classification diverse class several possible exciting extension 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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['Antoni Rosinol Vidal', 'Henri Rebecq', 'Timo Horstschaefer', 'Davide Scaramuzza']

1709.06310v4

Event cameras are bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. These cameras do not suffer from motion blur and have a very high dynamic range, which enables them to provide reliable visual information during high speed motions or in scenes characterized by high dynamic range. However, event cameras output only little information when the amount of motion is limited, such as in the case of almost still motion. Conversely, standard cameras provide instant and rich information about the environment most of the time (in low-speed and good lighting scenarios), but they fail severely in case of fast motions, or difficult lighting such as high dynamic range or low light scenes. In this paper, we present the first state estimation pipeline that leverages the complementary advantages of these two sensors by fusing in a tightly-coupled manner events, standard frames, and inertial measurements. We show on the publicly available Event Camera Dataset that our hybrid pipeline leads to an accuracy improvement of 130% over event-only pipelines, and 85% over standard-frames-only visual-inertial systems, while still being computationally tractable. Furthermore, we use our pipeline to demonstrate - to the best of our knowledge - the first autonomous quadrotor flight using an event camera for state estimation, unlocking flight scenarios that were not reachable with traditional visual-inertial odometry, such as low-light environments and high-dynamic range scenes.

Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios

http://arxiv.org/pdf/1709.06310v4

Title Ultimate SLAM Combining Events Images IMU Robust Visual SLAM HDR High Speed Scenarios Summary Event camera bioinspired vision sensor output pixellevel brightness change instead standard intensity frame camera suffer motion blur high dynamic range enables provide reliable visual information high speed motion scene characterized high dynamic range However event camera output little information amount motion limited case almost still motion Conversely standard camera provide instant rich information environment time lowspeed good lighting scenario fail severely case fast motion difficult lighting high dynamic range low light scene paper present first state estimation pipeline leverage complementary advantage two sensor fusing tightlycoupled manner event standard frame inertial measurement show publicly available Event Camera Dataset hybrid pipeline lead accuracy improvement 130 eventonly pipeline 85 standardframesonly visualinertial system still computationally tractable Furthermore use pipeline demonstrate best knowledge first autonomous quadrotor flight using event camera state estimation unlocking flight scenario reachable traditional visualinertial odometry lowlight environment highdynamic range scene 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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['Tadanobu Inoue', 'Subhajit Chaudhury', 'Giovanni De Magistris', 'Sakyasingha Dasgupta']

1709.06762v1

Robotic learning in simulation environments provides a faster, more scalable, and safer training methodology than learning directly with physical robots. Also, synthesizing images in a simulation environment for collecting large-scale image data is easy, whereas capturing camera images in the real world is time consuming and expensive. However, learning from only synthetic images may not achieve the desired performance in real environments due to the gap between synthetic and real images. We thus propose a method that transfers learned capability of detecting object position from a simulation environment to the real world. Our method enables us to use only a very limited dataset of real images while leveraging a large dataset of synthetic images using multiple variational autoencoders. It detects object positions 6 to 7 times more precisely than the baseline of directly learning from the dataset of the real images. Object position estimation under varying environmental conditions forms one of the underlying requirement for standard robotic manipulation tasks. We show that the proposed method performs robustly in different lighting conditions or with other distractor objects present for this requirement. Using this detected object position, we transfer pick-and-place or reaching tasks learned in a simulation environment to an actual physical robot without re-training.

Transfer learning from synthetic to real images using variational autoencoders for robotic applications

http://arxiv.org/pdf/1709.06762v1

Title Transfer learning synthetic real image using variational autoencoders robotic application Summary Robotic learning simulation environment provides faster scalable safer training methodology learning directly physical robot Also synthesizing image simulation environment collecting largescale image data easy whereas capturing camera image real world time consuming expensive However learning synthetic image may achieve desired performance real environment due gap synthetic real image thus propose method transfer learned capability detecting object position simulation environment real world method enables u use limited dataset real image leveraging large dataset synthetic image using multiple variational autoencoders detects object position 6 7 time precisely baseline directly learning dataset real image Object position estimation varying environmental condition form one underlying requirement standard robotic manipulation task show proposed method performs robustly different lighting condition distractor object present requirement Using detected object position transfer pickandplace reaching task learned simulation environment actual physical robot without retraining 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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['Andres Milioto', 'Philipp Lottes', 'Cyrill Stachniss']

1709.06764v2

Precision farming robots, which target to reduce the amount of herbicides that need to be brought out in the fields, must have the ability to identify crops and weeds in real time to trigger weeding actions. In this paper, we address the problem of CNN-based semantic segmentation of crop fields separating sugar beet plants, weeds, and background solely based on RGB data. We propose a CNN that exploits existing vegetation indexes and provides a classification in real time. Furthermore, it can be effectively re-trained to so far unseen fields with a comparably small amount of training data. We implemented and thoroughly evaluated our system on a real agricultural robot operating in different fields in Germany and Switzerland. The results show that our system generalizes well, can operate at around 20Hz, and is suitable for online operation in the fields.

Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs

http://arxiv.org/pdf/1709.06764v2

Title Realtime Semantic Segmentation Crop Weed Precision Agriculture Robots Leveraging Background Knowledge CNNs Summary Precision farming robot target reduce amount herbicide need brought field must ability identify crop weed real time trigger weeding action paper address problem CNNbased semantic segmentation crop field separating sugar beet plant weed background solely based RGB data propose CNN exploit existing vegetation index provides classification real time Furthermore effectively retrained far unseen field comparably small amount training data implemented thoroughly evaluated system real agricultural robot operating different field Germany Switzerland result show system generalizes well operate around 20Hz suitable online operation field 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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['Trung Pham', 'Thanh-Toan Do', 'Niko Sünderhauf', 'Ian Reid']

1709.07158v1

This paper presents SceneCut, a novel approach to jointly discover previously unseen objects and non-object surfaces using a single RGB-D image. SceneCut's joint reasoning over scene semantics and geometry allows a robot to detect and segment object instances in complex scenes where modern deep learning-based methods either fail to separate object instances, or fail to detect objects that were not seen during training. SceneCut automatically decomposes a scene into meaningful regions which either represent objects or scene surfaces. The decomposition is qualified by an unified energy function over objectness and geometric fitting. We show how this energy function can be optimized efficiently by utilizing hierarchical segmentation trees. Moreover, we leverage a pre-trained convolutional oriented boundary network to predict accurate boundaries from images, which are used to construct high-quality region hierarchies. We evaluate SceneCut on several different indoor environments, and the results show that SceneCut significantly outperforms all the existing methods.

SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes

http://arxiv.org/pdf/1709.07158v1

Title SceneCut Joint Geometric Object Segmentation Indoor Scenes Summary paper present SceneCut novel approach jointly discover previously unseen object nonobject surface using single RGBD image SceneCuts joint reasoning scene semantics geometry allows robot detect segment object instance complex scene modern deep learningbased method either fail separate object instance fail detect object seen training SceneCut automatically decomposes scene meaningful region either represent object scene surface decomposition qualified unified energy function objectness geometric fitting show energy function optimized efficiently utilizing hierarchical segmentation tree Moreover leverage pretrained convolutional oriented boundary network predict accurate boundary image used construct highquality region hierarchy evaluate SceneCut several different indoor environment result show SceneCut significantly outperforms existing 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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['Piergiorgio Caramazza', 'Alessandro Boccolini', 'Daniel Buschek', 'Matthias Hullin', 'Catherine Higham', 'Robert Henderson', 'Roderick Murray-Smith', 'Daniele Faccio']

1709.07244v1

Light scattered from multiple surfaces can be used to retrieve information of hidden environments. However, full three-dimensional retrieval of an object hidden from view by a wall has only been achieved with scanning systems and requires intensive computational processing of the retrieved data. Here we use a non-scanning, single-photon single-pixel detector in combination with an artificial neural network: this allows us to locate the position and to also simultaneously provide the actual identity of a hidden person, chosen from a database of people (N=3). Artificial neural networks applied to specific computational imaging problems can therefore enable novel imaging capabilities with hugely simplified hardware and processing times

Neural network identification of people hidden from view with a single-pixel, single-photon detector

http://arxiv.org/pdf/1709.07244v1

Title Neural network identification people hidden view singlepixel singlephoton detector Summary Light scattered multiple surface used retrieve information hidden environment However full threedimensional retrieval object hidden view wall achieved scanning system requires intensive computational processing retrieved data use nonscanning singlephoton singlepixel detector combination artificial neural network allows u locate position also simultaneously provide actual identity hidden person chosen database people N3 Artificial neural network applied specific computational imaging problem therefore enable novel imaging capability hugely simplified hardware processing time 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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['Thanh-Toan Do', 'Anh Nguyen', 'Ian Reid']

1709.07326v3

We propose AffordanceNet, a new deep learning approach to simultaneously detect multiple objects and their affordances from RGB images. Our AffordanceNet has two branches: an object detection branch to localize and classify the object, and an affordance detection branch to assign each pixel in the object to its most probable affordance label. The proposed framework employs three key components for effectively handling the multiclass problem in the affordance mask: a sequence of deconvolutional layers, a robust resizing strategy, and a multi-task loss function. The experimental results on the public datasets show that our AffordanceNet outperforms recent state-of-the-art methods by a fair margin, while its end-to-end architecture allows the inference at the speed of 150ms per image. This makes our AffordanceNet well suitable for real-time robotic applications. Furthermore, we demonstrate the effectiveness of AffordanceNet in different testing environments and in real robotic applications. The source code is available at https://github.com/nqanh/affordance-net

AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection

http://arxiv.org/pdf/1709.07326v3

Title AffordanceNet EndtoEnd Deep Learning Approach Object Affordance Detection Summary propose AffordanceNet new deep learning approach simultaneously detect multiple object affordances RGB image AffordanceNet two branch object detection branch localize classify object affordance detection branch assign pixel object probable affordance label proposed framework employ three key component effectively handling multiclass problem affordance mask sequence deconvolutional layer robust resizing strategy multitask loss function experimental result public datasets show AffordanceNet outperforms recent stateoftheart method fair margin endtoend architecture allows inference speed 150ms per image make AffordanceNet well suitable realtime robotic application Furthermore demonstrate effectiveness AffordanceNet different testing environment real robotic application source code available httpsgithubcomnqanhaffordancenet 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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['A. Milan', 'T. Pham', 'K. Vijay', 'D. Morrison', 'A. W. Tow', 'L. Liu', 'J. Erskine', 'R. Grinover', 'A. Gurman', 'T. Hunn', 'N. Kelly-Boxall', 'D. Lee', 'M. McTaggart', 'G. Rallos', 'A. Razjigaev', 'T. Rowntree', 'T. Shen', 'R. Smith', 'S. Wade-McCue', 'Z. Zhuang', 'C. Lehnert', 'G. Lin', 'I. Reid', 'P. Corke', 'J. Leitner']

1709.07665v1

We present our approach for robotic perception in cluttered scenes that led to winning the recent Amazon Robotics Challenge (ARC) 2017. Next to small objects with shiny and transparent surfaces, the biggest challenge of the 2017 competition was the introduction of unseen categories. In contrast to traditional approaches which require large collections of annotated data and many hours of training, the task here was to obtain a robust perception pipeline with only few minutes of data acquisition and training time. To that end, we present two strategies that we explored. One is a deep metric learning approach that works in three separate steps: semantic-agnostic boundary detection, patch classification and pixel-wise voting. The other is a fully-supervised semantic segmentation approach with efficient dataset collection. We conduct an extensive analysis of the two methods on our ARC 2017 dataset. Interestingly, only few examples of each class are sufficient to fine-tune even very deep convolutional neural networks for this specific task.

Semantic Segmentation from Limited Training Data

http://arxiv.org/pdf/1709.07665v1

Title Semantic Segmentation Limited Training Data Summary present approach robotic perception cluttered scene led winning recent Amazon Robotics Challenge ARC 2017 Next small object shiny transparent surface biggest challenge 2017 competition introduction unseen category contrast traditional approach require large collection annotated data many hour training task obtain robust perception pipeline minute data acquisition training time end present two strategy explored One deep metric learning approach work three separate step semanticagnostic boundary detection patch classification pixelwise voting fullysupervised semantic segmentation approach efficient dataset collection conduct extensive analysis two method ARC 2017 dataset Interestingly example class sufficient finetune even deep convolutional neural network specific 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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['Ribana Roscher', 'Bernd Uebbing', 'Jürgen Kusche']

1709.07681v1

Satellite radar altimetry is one of the most powerful techniques for measuring sea surface height variations, with applications ranging from operational oceanography to climate research. Over open oceans, altimeter return waveforms generally correspond to the Brown model, and by inversion, estimated shape parameters provide mean surface height and wind speed. However, in coastal areas or over inland waters, the waveform shape is often distorted by land influence, resulting in peaks or fast decaying trailing edges. As a result, derived sea surface heights are then less accurate and waveforms need to be reprocessed by sophisticated algorithms. To this end, this work suggests a novel Spatio-Temporal Altimetry Retracking (STAR) technique. We show that STAR enables the derivation of sea surface heights over the open ocean as well as over coastal regions of at least the same quality as compared to existing retracking methods, but for a larger number of cycles and thus retaining more useful data. Novel elements of our method are (a) integrating information from spatially and temporally neighboring waveforms through a conditional random field approach, (b) sub-waveform detection, where relevant sub-waveforms are separated from corrupted or non-relevant parts through a sparse representation approach, and (c) identifying the final best set of sea surfaces heights from multiple likely heights using Dijkstra's algorithm. We apply STAR to data from the Jason-1, Jason-2 and Envisat missions for study sites in the Gulf of Trieste, Italy and in the coastal region of the Ganges-Brahmaputra-Meghna estuary, Bangladesh. We compare to several established and recent retracking methods, as well as to tide gauge data. Our experiments suggest that the obtained sea surface heights are significantly less affected by outliers when compared to results obtained by other approaches.

STAR: Spatio-Temporal Altimeter Waveform Retracking using Sparse Representation and Conditional Random Fields

http://arxiv.org/pdf/1709.07681v1

Title STAR SpatioTemporal Altimeter Waveform Retracking using Sparse Representation Conditional Random Fields Summary Satellite radar altimetry one powerful technique measuring sea surface height variation application ranging operational oceanography climate research open ocean altimeter return waveform generally correspond Brown model inversion estimated shape parameter provide mean surface height wind speed However coastal area inland water waveform shape often distorted land influence resulting peak fast decaying trailing edge result derived sea surface height le accurate waveform need reprocessed sophisticated algorithm end work suggests novel SpatioTemporal Altimetry Retracking STAR technique show STAR enables derivation sea surface height open ocean well coastal region least quality compared existing retracking method larger number cycle thus retaining useful data Novel element method integrating information spatially temporally neighboring waveform conditional random field approach b subwaveform detection relevant subwaveforms separated corrupted nonrelevant part sparse representation approach c identifying final best set sea surface height multiple likely height using Dijkstras algorithm apply STAR data Jason1 Jason2 Envisat mission study site Gulf Trieste Italy coastal region GangesBrahmaputraMeghna estuary Bangladesh compare several established recent retracking method well tide gauge data experiment suggest obtained sea surface height significantly le affected outlier compared result obtained approach 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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['Xiao-Yun Zhou', 'Jianyu Lin', 'Celia Riga', 'Guang-Zhong Yang', 'Su-Lin Lee']

1709.07689v2

Robot-assisted deployment of fenestrated stent grafts in Fenestrated Endovascular Aortic Repair (FEVAR) requires accurate geometrical alignment. Currently, this process is guided by 2D fluoroscopy, which is uninformative and error prone. In this paper, a real-time framework is proposed to instantiate the 3D shape of a fenestrated stent graft based on only a single low-dose 2D fluoroscopic image. Firstly, the fenestrated stent graft was placed with markers. Secondly, the 3D pose of each stent segment was instantiated by the RPnP (Robust Perspective-n-Point) method. Thirdly, the 3D shape of the whole stent graft was instantiated via graft gap interpolation. Focal-Unet was proposed to segment the markers from 2D fluoroscopic images to achieve semi-automatic marker detection. The proposed framework was validated on five patient-specific 3D printed phantoms of aortic aneurysms and three stent grafts with new marker placements, showing an average distance error of 1-3mm and an average angle error of 4 degree.

Real-time 3D Shape Instantiation from Single Fluoroscopy Projection for Fenestrated Stent Graft Deployment

http://arxiv.org/pdf/1709.07689v2

Title Realtime 3D Shape Instantiation Single Fluoroscopy Projection Fenestrated Stent Graft Deployment Summary Robotassisted deployment fenestrated stent graft Fenestrated Endovascular Aortic Repair FEVAR requires accurate geometrical alignment Currently process guided 2D fluoroscopy uninformative error prone paper realtime framework proposed instantiate 3D shape fenestrated stent graft based single lowdose 2D fluoroscopic image Firstly fenestrated stent graft placed marker Secondly 3D pose stent segment instantiated RPnP Robust PerspectivenPoint method Thirdly 3D shape whole stent graft instantiated via graft gap interpolation FocalUnet proposed segment marker 2D fluoroscopic image achieve semiautomatic marker detection proposed framework validated five patientspecific 3D printed phantom aortic aneurysm three stent graft new marker placement showing average distance error 13mm average angle error 4 degree 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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['Krzysztof M. Czajkowski', 'Anna Pastuszczak', 'Rafał Kotyński']

1709.07739v2

Single-pixel imaging is an indirect imaging technique which utilizes simplified optical hardware and advanced computational methods. It offers novel solutions for hyper-spectral imaging, polarimetric imaging, three-dimensional imaging, holographic imaging, optical encryption and imaging through scattering media. The main limitations for its use come from relatively high measurement and reconstruction times. In this paper we propose to reduce the required signal acquisition time by using a novel sampling scheme based on a random selection of Morlet wavelets convolved with white noise. While such functions exhibit random properties, they are locally determined by Morlet wavelet parameters. The proposed method is equivalent to random sampling of the properly selected part of the feature space, which maps the measured images accurately both in the spatial and spatial frequency domains. We compare both numerically and experimentally the image quality obtained with our sampling protocol against widely-used sampling with Walsh-Hadamard or noiselet functions. The results show considerable improvement over the former methods, enabling single-pixel imaging at low compression rates on the order of a few percent.

Single-pixel imaging with Morlet wavelet correlated random patterns

http://arxiv.org/pdf/1709.07739v2

Title Singlepixel imaging Morlet wavelet correlated random pattern Summary Singlepixel imaging indirect imaging technique utilizes simplified optical hardware advanced computational method offer novel solution hyperspectral imaging polarimetric imaging threedimensional imaging holographic imaging optical encryption imaging scattering medium main limitation use come relatively high measurement reconstruction time paper propose reduce required signal acquisition time using novel sampling scheme based random selection Morlet wavelet convolved white noise function exhibit random property locally determined Morlet wavelet parameter proposed method equivalent random sampling properly selected part feature space map measured image accurately spatial spatial frequency domain compare numerically experimentally image quality obtained sampling protocol widelyused sampling WalshHadamard noiselet function result show considerable improvement former method enabling singlepixel imaging low compression rate order percent 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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['Vladimir Saveljev', 'Sung-Kyu Kim']

1709.07745v1

The article presents the experimental measurements of the amplitude of the moir\'e patterns in a digital autostereoscopic barrier-type 3D display across a wide angular range with a small increment. The period and orientation of the moir\'e patterns were also measured as functions of the angle. Simultaneous branches are observed and analyzed. The theoretical interpretation is also given. The results can help preventing or minimizing the moir\'e effect in displays.

Measurement of amplitude of the moiré patterns in digital autostereoscopic 3D display

http://arxiv.org/pdf/1709.07745v1

Title Measurement amplitude moiré pattern digital autostereoscopic 3D display Summary article present experimental measurement amplitude moire pattern digital autostereoscopic barriertype 3D display across wide angular range small increment period orientation moire pattern also measured function angle Simultaneous branch observed analyzed theoretical interpretation also given result help preventing minimizing moire effect display 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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['Chamberlain Fong']

1709.07875v1

We present and discuss different algorithms for converting rectangular imagery into elliptical regions. We will focus primarily on methods that use mathematical mappings with explicit and invertible equations. We will also present different post-processing effects that could be applied to enhance the resulting images and minimize distortion.

Elliptification of Rectangular Imagery

http://arxiv.org/pdf/1709.07875v1

Title Elliptification Rectangular Imagery Summary present discus different algorithm converting rectangular imagery elliptical region focus primarily method use mathematical mapping explicit invertible equation also present different postprocessing effect could applied enhance resulting image minimize 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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['Abhimanyu Dubey', 'Sumeet Agarwal']

1709.07914v1

The study of virality and information diffusion online is a topic gaining traction rapidly in the computational social sciences. Computer vision and social network analysis research have also focused on understanding the impact of content and information diffusion in making content viral, with prior approaches not performing significantly well as other traditional classification tasks. In this paper, we present a novel pairwise reformulation of the virality prediction problem as an attribute prediction task and develop a novel algorithm to model image virality on online media using a pairwise neural network. Our model provides significant insights into the features that are responsible for promoting virality and surpasses the existing state-of-the-art by a 12% average improvement in prediction. We also investigate the effect of external category supervision on relative attribute prediction and observe an increase in prediction accuracy for the same across several attribute learning datasets.

Modeling Image Virality with Pairwise Spatial Transformer Networks

http://arxiv.org/pdf/1709.07914v1

Title Modeling Image Virality Pairwise Spatial Transformer Networks Summary study virality information diffusion online topic gaining traction rapidly computational social science Computer vision social network analysis research also focused understanding impact content information diffusion making content viral prior approach performing significantly well traditional classification task paper present novel pairwise reformulation virality prediction problem attribute prediction task develop novel algorithm model image virality online medium using pairwise neural network model provides significant insight feature responsible promoting virality surpasses existing stateoftheart 12 average improvement prediction also investigate effect external category supervision relative attribute prediction observe increase prediction accuracy across several attribute learning 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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['Xin Jie Tang', 'Yong Haur Tay', 'Nordahlia Abdullah Siam', 'Seng Choon Lim']

1709.08154v1

Wood Identification has never been more important to serve the purpose of global forest species protection and timber regulation. Macroscopic level wood identification practiced by wood anatomists can identify wood up to genus level. This is sufficient to serve as a frontline identification to fight against illegal wood logging and timber trade for law enforcement authority. However, frontline enforcement official may lack of the accuracy and confidence of a well trained wood anatomist. Hence, computer assisted method such as machine vision methods are developed to do rapid field identification for law enforcement official. In this paper, we proposed a rapid and robust macroscopic wood identification system using machine vision method with off-the-shelf smartphone and retrofitted macro-lens. Our system is cost effective, easily accessible, fast and scalable at the same time provides human-level accuracy on identification. Camera-enabled smartphone with Internet connectivity coupled with a macro-lens provides a simple and effective digital acquisition of macroscopic wood images which are essential for macroscopic wood identification. The images are immediately streamed to a cloud server via Internet connection for identification which are done within seconds.

Rapid and Robust Automated Macroscopic Wood Identification System using Smartphone with Macro-lens

http://arxiv.org/pdf/1709.08154v1

Title Rapid Robust Automated Macroscopic Wood Identification System using Smartphone Macrolens Summary Wood Identification never important serve purpose global forest specie protection timber regulation Macroscopic level wood identification practiced wood anatomist identify wood genus level sufficient serve frontline identification fight illegal wood logging timber trade law enforcement authority However frontline enforcement official may lack accuracy confidence well trained wood anatomist Hence computer assisted method machine vision method developed rapid field identification law enforcement official paper proposed rapid robust macroscopic wood identification system using machine vision method offtheshelf smartphone retrofitted macrolens system cost effective easily accessible fast scalable time provides humanlevel accuracy identification Cameraenabled smartphone Internet connectivity coupled macrolens provides simple effective digital acquisition macroscopic wood image essential macroscopic wood identification image immediately streamed cloud server via Internet connection identification done within 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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['Xin Jin', 'Shuyun Zhu', 'Chaoen Xiao', 'Hongbo Sun', 'Xiaodong Li', 'Geng Zhao', 'Shiming Ge']

1709.08364v1

In the coming Virtual/Augmented Reality (VR/AR) era, 3D contents will be popularized just as images and videos today. The security and privacy of these 3D contents should be taken into consideration. 3D contents contain surface models and solid models. The surface models include point clouds, meshes and textured models. Previous work mainly focus on encryption of solid models, point clouds and meshes. This work focuses on the most complicated 3D textured model. We propose a 3D Lu chaotic mapping based encryption method of 3D textured model. We encrypt the vertexes, the polygons and the textures of 3D models separately using the 3D Lu chaotic mapping. Then the encrypted vertices, edges and texture maps are composited together to form the final encrypted 3D textured model. The experimental results reveal that our method can encrypt and decrypt 3D textured models correctly. In addition, our method can resistant several attacks such as brute-force attack and statistic attack.

3D Textured Model Encryption via 3D Lu Chaotic Mapping

http://arxiv.org/pdf/1709.08364v1

Title 3D Textured Model Encryption via 3D Lu Chaotic Mapping Summary coming VirtualAugmented Reality VRAR era 3D content popularized image video today security privacy 3D content taken consideration 3D content contain surface model solid model surface model include point cloud mesh textured model Previous work mainly focus encryption solid model point cloud mesh work focus complicated 3D textured model propose 3D Lu chaotic mapping based encryption method 3D textured model encrypt vertex polygon texture 3D model separately using 3D Lu chaotic mapping encrypted vertex edge texture map composited together form final encrypted 3D textured model experimental result reveal method encrypt decrypt 3D textured model correctly addition method resistant several attack bruteforce attack statistic attack 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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['Sen Wang', 'Ronald Clark', 'Hongkai Wen', 'Niki Trigoni']

1709.08429v1

This paper studies monocular visual odometry (VO) problem. Most of existing VO algorithms are developed under a standard pipeline including feature extraction, feature matching, motion estimation, local optimisation, etc. Although some of them have demonstrated superior performance, they usually need to be carefully designed and specifically fine-tuned to work well in different environments. Some prior knowledge is also required to recover an absolute scale for monocular VO. This paper presents a novel end-to-end framework for monocular VO by using deep Recurrent Convolutional Neural Networks (RCNNs). Since it is trained and deployed in an end-to-end manner, it infers poses directly from a sequence of raw RGB images (videos) without adopting any module in the conventional VO pipeline. Based on the RCNNs, it not only automatically learns effective feature representation for the VO problem through Convolutional Neural Networks, but also implicitly models sequential dynamics and relations using deep Recurrent Neural Networks. Extensive experiments on the KITTI VO dataset show competitive performance to state-of-the-art methods, verifying that the end-to-end Deep Learning technique can be a viable complement to the traditional VO systems.

DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks

http://arxiv.org/pdf/1709.08429v1

Title DeepVO Towards EndtoEnd Visual Odometry Deep Recurrent Convolutional Neural Networks Summary paper study monocular visual odometry VO problem existing VO algorithm developed standard pipeline including feature extraction feature matching motion estimation local optimisation etc Although demonstrated superior performance usually need carefully designed specifically finetuned work well different environment prior knowledge also required recover absolute scale monocular VO paper present novel endtoend framework monocular VO using deep Recurrent Convolutional Neural Networks RCNNs Since trained deployed endtoend manner infers pose directly sequence raw RGB image video without adopting module conventional VO pipeline Based RCNNs automatically learns effective feature representation VO problem Convolutional Neural Networks also implicitly model sequential dynamic relation using deep Recurrent Neural Networks Extensive experiment KITTI VO dataset show competitive performance stateoftheart method verifying endtoend Deep Learning technique viable complement traditional VO 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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['Daniel D. Morris', 'Brian Colonna', 'Paul Haley']

1709.08515v1

Detecting moving vehicles and people is crucial for safe operation of UGVs but is challenging in cluttered, real world environments. We propose a registration technique that enables objects to be robustly matched and tracked, and hence movers to be detected even in high clutter. Range data are acquired using a 2D scanning Ladar from a moving platform. These are automatically clustered into objects and modeled using a surface density function. A Bhattacharya similarity is optimized to register subsequent views of each object enabling good discrimination and tracking, and hence mover detection.

LADAR-Based Mover Detection from Moving Vehicles

http://arxiv.org/pdf/1709.08515v1

Title LADARBased Mover Detection Moving Vehicles Summary Detecting moving vehicle people crucial safe operation UGVs challenging cluttered real world environment propose registration technique enables object robustly matched tracked hence mover detected even high clutter Range data acquired using 2D scanning Ladar moving platform automatically clustered object modeled using surface density function Bhattacharya similarity optimized register subsequent view object enabling good discrimination tracking hence mover detection 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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['Md Jahidul Islam', 'Marc Ho', 'Junaed Sattar']

1709.08772v8

This paper presents a real-time programming and parameter reconfiguration method for autonomous underwater robots in human-robot collaborative tasks. Using a set of intuitive and meaningful hand gestures, we develop a syntactically simple framework that is computationally more efficient than a complex, grammar-based approach. In the proposed framework, a convolutional neural network is trained to provide accurate hand gesture recognition; subsequently, a finite-state machine-based deterministic model performs efficient gesture-to-instruction mapping, and further improves robustness of the interaction scheme. The key aspect of this framework is that it can be easily adopted by divers for communicating simple instructions to underwater robots without using artificial tags such as fiducial markers, or requiring them to memorize a potentially complex set of language rules. Extensive experiments are performed both on field-trial data and through simulation, which demonstrate the robustness, efficiency, and portability of this framework in a number of different scenarios. Finally, a user interaction study is presented that illustrates the gain in usability of our proposed interaction framework compared to the existing methods for underwater domains.

Dynamic Reconfiguration of Mission Parameters in Underwater Human-Robot Collaboration

http://arxiv.org/pdf/1709.08772v8

Title Dynamic Reconfiguration Mission Parameters Underwater HumanRobot Collaboration Summary paper present realtime programming parameter reconfiguration method autonomous underwater robot humanrobot collaborative task Using set intuitive meaningful hand gesture develop syntactically simple framework computationally efficient complex grammarbased approach proposed framework convolutional neural network trained provide accurate hand gesture recognition subsequently finitestate machinebased deterministic model performs efficient gesturetoinstruction mapping improves robustness interaction scheme key aspect framework easily adopted diver communicating simple instruction underwater robot without using artificial tag fiducial marker requiring memorize potentially complex set language rule Extensive experiment performed fieldtrial data simulation demonstrate robustness efficiency portability framework number different scenario Finally user interaction study presented illustrates gain usability proposed interaction framework compared existing method underwater 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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['Xiang Zhang', 'Lina Yao', 'Quan Z. Sheng', 'Salil S. Kanhere', 'Tao Gu', 'Dalin Zhang']

1709.08820v1

An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots. More specifically, motor imagery EEG (MI-EEG), which reflects a subjects active intent, is attracting increasing attention for a variety of BCI applications. Accurate classification of MI-EEG signals while essential for effective operation of BCI systems, is challenging due to the significant noise inherent in the signals and the lack of informative correlation between the signals and brain activities. In this paper, we propose a novel deep neural network based learning framework that affords perceptive insights into the relationship between the MI-EEG data and brain activities. We design a joint convolutional recurrent neural network that simultaneously learns robust high-level feature presentations through low-dimensional dense embeddings from raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various artifacts such as background activities. The proposed approach has been evaluated extensively on a large- scale public MI-EEG dataset and a limited but easy-to-deploy dataset collected in our lab. The results show that our approach outperforms a series of baselines and the competitive state-of-the- art methods, yielding a classification accuracy of 95.53%. The applicability of our proposed approach is further demonstrated with a practical BCI system for typing.

Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

http://arxiv.org/pdf/1709.08820v1

Title Converting Thoughts Texts Enabling Brain Typing via Deep Feature Learning EEG Signals Summary electroencephalography EEG based Brain Computer Interface BCI enables people communicate outside world interpreting EEG signal brain interact device wheelchair intelligent robot specifically motor imagery EEG MIEEG reflects subject active intent attracting increasing attention variety BCI application Accurate classification MIEEG signal essential effective operation BCI system challenging due significant noise inherent signal lack informative correlation signal brain activity paper propose novel deep neural network based learning framework affords perceptive insight relationship MIEEG data brain activity design joint convolutional recurrent neural network simultaneously learns robust highlevel feature presentation lowdimensional dense embeddings raw MIEEG signal also employ Autoencoder layer eliminate various artifact background activity proposed approach evaluated extensively large scale public MIEEG dataset limited easytodeploy dataset collected lab result show approach outperforms series baseline competitive stateofthe art method yielding classification accuracy 9553 applicability proposed approach demonstrated practical BCI system typing 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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['Sandeep Konam', 'Stephanie Rosenthal', 'Manuela Veloso']

1709.08831v1

Our CoBot robots have successfully performed a variety of service tasks in our multi-building environment including accompanying people to meetings and delivering objects to offices due to its navigation and localization capabilities. However, they lack the capability to visually search over desks and other confined locations for an object of interest. Conversely, an inexpensive GPS-denied quadcopter platform such as the Parrot ARDrone 2.0 could perform this object search task if it had access to reasonable localization. In this paper, we propose the concept of coordination between CoBot and the Parrot ARDrone 2.0 to perform service-based object search tasks, in which CoBot localizes and navigates to the general search areas carrying the ARDrone and the ARDrone searches locally for objects. We propose a vision-based moving target navigation algorithm that enables the ARDrone to localize with respect to CoBot, search for objects, and return to the CoBot for future searches. We demonstrate our algorithm in indoor environments on several search trajectories.

UAV and Service Robot Coordination for Indoor Object Search Tasks

http://arxiv.org/pdf/1709.08831v1

Title UAV Service Robot Coordination Indoor Object Search Tasks Summary CoBot robot successfully performed variety service task multibuilding environment including accompanying people meeting delivering object office due navigation localization capability However lack capability visually search desk confined location object interest Conversely inexpensive GPSdenied quadcopter platform Parrot ARDrone 20 could perform object search task access reasonable localization paper propose concept coordination CoBot Parrot ARDrone 20 perform servicebased object search task CoBot localizes navigates general search area carrying ARDrone ARDrone search locally object propose visionbased moving target navigation algorithm enables ARDrone localize respect CoBot search object return CoBot future search demonstrate algorithm indoor environment several search trajectory 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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['Timo Lüddecke', 'Florentin Wörgötter']

1709.08872v1

An autonomous robot should be able to evaluate the affordances that are offered by a given situation. Here we address this problem by designing a system that can densely predict affordances given only a single 2D RGB image. This is achieved with a convolutional neural network (ResNet), which we combine with refinement modules recently proposed for addressing semantic image segmentation. We define a novel cost function, which is able to handle (potentially multiple) affordances of objects and their parts in a pixel-wise manner even in the case of incomplete data. We perform qualitative as well as quantitative evaluations with simulated and real data assessing 15 different affordances. In general, we find that affordances, which are well-enough represented in the training data, are correctly recognized with a substantial fraction of correctly assigned pixels. Furthermore, we show that our model outperforms several baselines. Hence, this method can give clear action guidelines for a robot.

Learning to Label Affordances from Simulated and Real Data

http://arxiv.org/pdf/1709.08872v1

Title Learning Label Affordances Simulated Real Data Summary autonomous robot able evaluate affordances offered given situation address problem designing system densely predict affordances given single 2D RGB image achieved convolutional neural network ResNet combine refinement module recently proposed addressing semantic image segmentation define novel cost function able handle potentially multiple affordances object part pixelwise manner even case incomplete data perform qualitative well quantitative evaluation simulated real data assessing 15 different affordances general find affordances wellenough represented training data correctly recognized substantial fraction correctly assigned pixel Furthermore show model outperforms several baseline Hence method give clear action guideline robot 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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['Xiang Zhang', 'Lina Yao', 'Dalin Zhang', 'Xianzhi Wang', 'Quan Z. Sheng', 'Tao Gu']

1709.09077v1

An electroencephalography (EEG) based brain activity recognition is a fundamental field of study for a number of significant applications such as intention prediction, appliance control, and neurological disease diagnosis in smart home and smart healthcare domains. Existing techniques mostly focus on binary brain activity recognition for a single person, which limits their deployment in wider and complex practical scenarios. Therefore, multi-person and multi-class brain activity recognition has obtained popularity recently. Another challenge faced by brain activity recognition is the low recognition accuracy due to the massive noises and the low signal-to-noise ratio in EEG signals. Moreover, the feature engineering in EEG processing is time-consuming and highly re- lies on the expert experience. In this paper, we attempt to solve the above challenges by proposing an approach which has better EEG interpretation ability via raw Electroencephalography (EEG) signal analysis for multi-person and multi-class brain activity recognition. Specifically, we analyze inter-class and inter-person EEG signal characteristics, based on which to capture the discrepancy of inter-class EEG data. Then, we adopt an Autoencoder layer to automatically refine the raw EEG signals by eliminating various artifacts. We evaluate our approach on both a public and a local EEG datasets and conduct extensive experiments to explore the effect of several factors (such as normalization methods, training data size, and Autoencoder hidden neuron size) on the recognition results. The experimental results show that our approach achieves a high accuracy comparing to competitive state-of-the-art methods, indicating its potential in promoting future research on multi-person EEG recognition.

Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis

http://arxiv.org/pdf/1709.09077v1

Title MultiPerson Brain Activity Recognition via Comprehensive EEG Signal Analysis Summary electroencephalography EEG based brain activity recognition fundamental field study number significant application intention prediction appliance control neurological disease diagnosis smart home smart healthcare domain Existing technique mostly focus binary brain activity recognition single person limit deployment wider complex practical scenario Therefore multiperson multiclass brain activity recognition obtained popularity recently Another challenge faced brain activity recognition low recognition accuracy due massive noise low signaltonoise ratio EEG signal Moreover feature engineering EEG processing timeconsuming highly lie expert experience paper attempt solve challenge proposing approach better EEG interpretation ability via raw Electroencephalography EEG signal analysis multiperson multiclass brain activity recognition Specifically analyze interclass interperson EEG signal characteristic based capture discrepancy interclass EEG data adopt Autoencoder layer automatically refine raw EEG signal eliminating various artifact evaluate approach public local EEG datasets conduct extensive experiment explore effect several factor normalization method training data size Autoencoder hidden neuron size recognition result experimental result show approach achieves high accuracy comparing competitive stateoftheart method indicating potential promoting future research multiperson EEG recognition 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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['Ryota Hinami', 'Yusuke Matsui', "Shin'ichi Satoh"]

1709.09106v1

Region-based image retrieval (RBIR) technique is revisited. In early attempts at RBIR in the late 90s, researchers found many ways to specify region-based queries and spatial relationships; however, the way to characterize the regions, such as by using color histograms, were very poor at that time. Here, we revisit RBIR by incorporating semantic specification of objects and intuitive specification of spatial relationships. Our contributions are the following. First, to support multiple aspects of semantic object specification (category, instance, and attribute), we propose a multitask CNN feature that allows us to use deep learning technique and to jointly handle multi-aspect object specification. Second, to help users specify spatial relationships among objects in an intuitive way, we propose recommendation techniques of spatial relationships. In particular, by mining the search results, a system can recommend feasible spatial relationships among the objects. The system also can recommend likely spatial relationships by assigned object category names based on language prior. Moreover, object-level inverted indexing supports very fast shortlist generation, and re-ranking based on spatial constraints provides users with instant RBIR experiences.

Region-Based Image Retrieval Revisited

http://arxiv.org/pdf/1709.09106v1

Title RegionBased Image Retrieval Revisited Summary Regionbased image retrieval RBIR technique revisited early attempt RBIR late 90 researcher found many way specify regionbased query spatial relationship however way characterize region using color histogram poor time revisit RBIR incorporating semantic specification object intuitive specification spatial relationship contribution following First support multiple aspect semantic object specification category instance attribute propose multitask CNN feature allows u use deep learning technique jointly handle multiaspect object specification Second help user specify spatial relationship among object intuitive way propose recommendation technique spatial relationship particular mining search result system recommend feasible spatial relationship among object system also recommend likely spatial relationship assigned object category name based language prior Moreover objectlevel inverted indexing support fast shortlist generation reranking based spatial constraint provides user instant RBIR experience 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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['Dylan Campbell', 'Lars Petersson', 'Laurent Kneip', 'Hongdong Li']

1709.09384v1

Estimating the 6-DoF pose of a camera from a single image relative to a pre-computed 3D point-set is an important task for many computer vision applications. Perspective-n-Point (PnP) solvers are routinely used for camera pose estimation, provided that a good quality set of 2D-3D feature correspondences are known beforehand. However, finding optimal correspondences between 2D key-points and a 3D point-set is non-trivial, especially when only geometric (position) information is known. Existing approaches to the simultaneous pose and correspondence problem use local optimisation, and are therefore unlikely to find the optimal solution without a good pose initialisation, or introduce restrictive assumptions. Since a large proportion of outliers are common for this problem, we instead propose a globally-optimal inlier set cardinality maximisation approach which jointly estimates optimal camera pose and optimal correspondences. Our approach employs branch-and-bound to search the 6D space of camera poses, guaranteeing global optimality without requiring a pose prior. The geometry of SE(3) is used to find novel upper and lower bounds for the number of inliers and local optimisation is integrated to accelerate convergence. The evaluation empirically supports the optimality proof and shows that the method performs much more robustly than existing approaches, including on a large-scale outdoor data-set.

Globally-Optimal Inlier Set Maximisation for Simultaneous Camera Pose and Feature Correspondence

http://arxiv.org/pdf/1709.09384v1

Title GloballyOptimal Inlier Set Maximisation Simultaneous Camera Pose Feature Correspondence Summary Estimating 6DoF pose camera single image relative precomputed 3D pointset important task many computer vision application PerspectivenPoint PnP solver routinely used camera pose estimation provided good quality set 2D3D feature correspondence known beforehand However finding optimal correspondence 2D keypoints 3D pointset nontrivial especially geometric position information known Existing approach simultaneous pose correspondence problem use local optimisation therefore unlikely find optimal solution without good pose initialisation introduce restrictive assumption Since large proportion outlier common problem instead propose globallyoptimal inlier set cardinality maximisation approach jointly estimate optimal camera pose optimal correspondence approach employ branchandbound search 6D space camera pose guaranteeing global optimality without requiring pose prior geometry SE3 used find novel upper lower bound number inliers local optimisation integrated accelerate convergence evaluation empirically support optimality proof show method performs much robustly existing approach including largescale outdoor dataset 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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['Yuanming Hu', 'Hao He', 'Chenxi Xu', 'Baoyuan Wang', 'Stephen Lin']

1709.09602v2

Retouching can significantly elevate the visual appeal of photos, but many casual photographers lack the expertise to do this well. To address this problem, previous works have proposed automatic retouching systems based on supervised learning from paired training images acquired before and after manual editing. As it is difficult for users to acquire paired images that reflect their retouching preferences, we present in this paper a deep learning approach that is instead trained on unpaired data, namely a set of photographs that exhibits a retouching style the user likes, which is much easier to collect. Our system is formulated using deep convolutional neural networks that learn to apply different retouching operations on an input image. Network training with respect to various types of edits is enabled by modeling these retouching operations in a unified manner as resolution-independent differentiable filters. To apply the filters in a proper sequence and with suitable parameters, we employ a deep reinforcement learning approach that learns to make decisions on what action to take next, given the current state of the image. In contrast to many deep learning systems, ours provides users with an understandable solution in the form of conventional retouching edits, rather than just a "black-box" result. Through quantitative comparisons and user studies, we show that this technique generates retouching results consistent with the provided photo set.

Exposure: A White-Box Photo Post-Processing Framework

http://arxiv.org/pdf/1709.09602v2

Title Exposure WhiteBox Photo PostProcessing Framework Summary Retouching significantly elevate visual appeal photo many casual photographer lack expertise well address problem previous work proposed automatic retouching system based supervised learning paired training image acquired manual editing difficult user acquire paired image reflect retouching preference present paper deep learning approach instead trained unpaired data namely set photograph exhibit retouching style user like much easier collect system formulated using deep convolutional neural network learn apply different retouching operation input image Network training respect various type edits enabled modeling retouching operation unified manner resolutionindependent differentiable filter apply filter proper sequence suitable parameter employ deep reinforcement learning approach learns make decision action take next given current state image contrast many deep learning system provides user understandable solution form conventional retouching edits rather blackbox result quantitative comparison user study show technique generates retouching result consistent provided photo set 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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['Gilad Lerman', 'Yunpeng Shi', 'Teng Zhang']

1709.09683v2

We establish exact recovery for the Least Unsquared Deviations (LUD) algorithm of \"{O}zyesil and Singer. More precisely, we show that for sufficiently many cameras with given corrupted pairwise directions, where both camera locations and pairwise directions are generated by a special probabilistic model, the LUD algorithm exactly recovers the camera locations with high probability. A similar exact recovery guarantee was established for the ShapeFit algorithm by Hand, Lee and Voroninski, but with typically less corruption.

Exact Camera Location Recovery by Least Unsquared Deviations

http://arxiv.org/pdf/1709.09683v2

Title Exact Camera Location Recovery Least Unsquared Deviations Summary establish exact recovery Least Unsquared Deviations LUD algorithm Ozyesil Singer precisely show sufficiently many camera given corrupted pairwise direction camera location pairwise direction generated special probabilistic model LUD algorithm exactly recovers camera location high probability similar exact recovery guarantee established ShapeFit algorithm Hand Lee Voroninski typically le corruption 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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['Abel Gawel', 'Carlo Del Don', 'Roland Siegwart', 'Juan Nieto', 'Cesar Cadena']

1709.09905v3

Global registration of multi-view robot data is a challenging task. Appearance-based global localization approaches often fail under drastic view-point changes, as representations have limited view-point invariance. This work is based on the idea that human-made environments contain rich semantics which can be used to disambiguate global localization. Here, we present X-View, a Multi-View Semantic Global Localization system. X-View leverages semantic graph descriptor matching for global localization, enabling localization under drastically different view-points. While the approach is general in terms of the semantic input data, we present and evaluate an implementation on visual data. We demonstrate the system in experiments on the publicly available SYNTHIA dataset, on a realistic urban dataset recorded with a simulator, and on real-world StreetView data. Our findings show that X-View is able to globally localize aerial-to-ground, and ground-to-ground robot data of drastically different view-points. Our approach achieves an accuracy of up to 85 % on global localizations in the multi-view case, while the benchmarked baseline appearance-based methods reach up to 75 %.

X-View: Graph-Based Semantic Multi-View Localization

http://arxiv.org/pdf/1709.09905v3

Title XView GraphBased Semantic MultiView Localization Summary Global registration multiview robot data challenging task Appearancebased global localization approach often fail drastic viewpoint change representation limited viewpoint invariance work based idea humanmade environment contain rich semantics used disambiguate global localization present XView MultiView Semantic Global Localization system XView leverage semantic graph descriptor matching global localization enabling localization drastically different viewpoint approach general term semantic input data present evaluate implementation visual data demonstrate system experiment publicly available SYNTHIA dataset realistic urban dataset recorded simulator realworld StreetView data finding show XView able globally localize aerialtoground groundtoground robot data drastically different viewpoint approach achieves accuracy 85 global localization multiview case benchmarked baseline appearancebased method reach 75 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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['Li Sun', 'Zhi Yan', 'Sergi Molina Mellado', 'Marc Hanheide', 'Tom Duckett']

1710.00126v1

This paper presents a novel 3DOF pedestrian trajectory prediction approach for autonomous mobile service robots. While most previously reported methods are based on learning of 2D positions in monocular camera images, our approach uses range-finder sensors to learn and predict 3DOF pose trajectories (i.e. 2D position plus 1D rotation within the world coordinate system). Our approach, T-Pose-LSTM (Temporal 3DOF-Pose Long-Short-Term Memory), is trained using long-term data from real-world robot deployments and aims to learn context-dependent (environment- and time-specific) human activities. Our approach incorporates long-term temporal information (i.e. date and time) with short-term pose observations as input. A sequence-to-sequence LSTM encoder-decoder is trained, which encodes observations into LSTM and then decodes as predictions. For deployment, it can perform on-the-fly prediction in real-time. Instead of using manually annotated data, we rely on a robust human detection, tracking and SLAM system, providing us with examples in a global coordinate system. We validate the approach using more than 15K pedestrian trajectories recorded in a care home environment over a period of three months. The experiment shows that the proposed T-Pose-LSTM model advances the state-of-the-art 2D-based method for human trajectory prediction in long-term mobile robot deployments.

3DOF Pedestrian Trajectory Prediction Learned from Long-Term Autonomous Mobile Robot Deployment Data

http://arxiv.org/pdf/1710.00126v1

Title 3DOF Pedestrian Trajectory Prediction Learned LongTerm Autonomous Mobile Robot Deployment Data Summary paper present novel 3DOF pedestrian trajectory prediction approach autonomous mobile service robot previously reported method based learning 2D position monocular camera image approach us rangefinder sensor learn predict 3DOF pose trajectory ie 2D position plus 1D rotation within world coordinate system approach TPoseLSTM Temporal 3DOFPose LongShortTerm Memory trained using longterm data realworld robot deployment aim learn contextdependent environment timespecific human activity approach incorporates longterm temporal information ie date time shortterm pose observation input sequencetosequence LSTM encoderdecoder trained encodes observation LSTM decodes prediction deployment perform onthefly prediction realtime Instead using manually annotated data rely robust human detection tracking SLAM system providing u example global coordinate system validate approach using 15K pedestrian trajectory recorded care home environment period three month experiment show proposed TPoseLSTM model advance stateoftheart 2Dbased method human trajectory prediction longterm mobile robot deployment 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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['Anh Nguyen', 'Dimitrios Kanoulas', 'Luca Muratore', 'Darwin G. Caldwell', 'Nikos G. Tsagarakis']

1710.00290v1

We present a new method to translate videos to commands for robotic manipulation using Deep Recurrent Neural Networks (RNN). Our framework first extracts deep features from the input video frames with a deep Convolutional Neural Networks (CNN). Two RNN layers with an encoder-decoder architecture are then used to encode the visual features and sequentially generate the output words as the command. We demonstrate that the translation accuracy can be improved by allowing a smooth transaction between two RNN layers and using the state-of-the-art feature extractor. The experimental results on our new challenging dataset show that our approach outperforms recent methods by a fair margin. Furthermore, we combine the proposed translation module with the vision and planning system to let a robot perform various manipulation tasks. Finally, we demonstrate the effectiveness of our framework on a full-size humanoid robot WALK-MAN.

Translating Videos to Commands for Robotic Manipulation with Deep Recurrent Neural Networks

http://arxiv.org/pdf/1710.00290v1

Title Translating Videos Commands Robotic Manipulation Deep Recurrent Neural Networks Summary present new method translate video command robotic manipulation using Deep Recurrent Neural Networks RNN framework first extract deep feature input video frame deep Convolutional Neural Networks CNN Two RNN layer encoderdecoder architecture used encode visual feature sequentially generate output word command demonstrate translation accuracy improved allowing smooth transaction two RNN layer using stateoftheart feature extractor experimental result new challenging dataset show approach outperforms recent method fair margin Furthermore combine proposed translation module vision planning system let robot perform various manipulation task Finally demonstrate effectiveness framework fullsize humanoid robot WALKMAN 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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['Yuzhen Lu']

1710.00620v2

Image de-blurring is important in many cases of imaging a real scene or object by a camera. This project focuses on de-blurring an image distorted by an out-of-focus blur through a simulation study. A pseudo-inverse filter is first explored but it fails because of severe noise amplification. Then Tikhonov regularization methods are employed, which produce greatly improved results compared to the pseudo-inverse filter. In Tikhonov regularization, the choice of the regularization parameter plays a critical rule in obtaining a high-quality image, and the regularized solutions possess a semi-convergence property. The best result, with the relative restoration error of 8.49%, is achieved when the prescribed discrepancy principle is used to decide an optimal value. Furthermore, an iterative method, Conjugated Gradient, is employed for image de-blurring, which is fast in computation and leads to an even better result with the relative restoration error of 8.22%. The number of iteration in CG acts as a regularization parameter, and the iterates have a semi-convergence property as well.

Out-of-focus Blur: Image De-blurring

http://arxiv.org/pdf/1710.00620v2

Title Outoffocus Blur Image Deblurring Summary Image deblurring important many case imaging real scene object camera project focus deblurring image distorted outoffocus blur simulation study pseudoinverse filter first explored fails severe noise amplification Tikhonov regularization method employed produce greatly improved result compared pseudoinverse filter Tikhonov regularization choice regularization parameter play critical rule obtaining highquality image regularized solution posse semiconvergence property best result relative restoration error 849 achieved prescribed discrepancy principle used decide optimal value Furthermore iterative method Conjugated Gradient employed image deblurring fast computation lead even better result relative restoration error 822 number iteration CG act regularization parameter iterates semiconvergence property 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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['Joan Duran', 'Antoni Buades']

1710.00672v1

Pansharpening techniques aim at fusing the structural detail of the panchromatic and the color accuracy of the low-resolution spectral bands provided by Earth observation satellites to produce a high-resolution multispectral image. A large number of methods have been proposed in the literature, most of which were developed based on certain assumptions about the acquisition system. If any of these assumptions are not fulfilled, the quality of the fused products may be seriously compromised. We propose a post-processing restoration strategy to improve the quality of pansharpened images. In a first stage, we introduce a nonlocal variational model for filtering the chromatic components conditionally to the geometry of the panchromatic. In the second stage, the structural component is replaced by the locally histogram-matched panchromatic for spatial enhancement. An exhaustive performance evaluation of the proposed restoration strategy illustrates its efficiency.

Conditional Chromatic Filtering with Spatial Enhancement for Restoring Pansharpened Images

http://arxiv.org/pdf/1710.00672v1

Title Conditional Chromatic Filtering Spatial Enhancement Restoring Pansharpened Images Summary Pansharpening technique aim fusing structural detail panchromatic color accuracy lowresolution spectral band provided Earth observation satellite produce highresolution multispectral image large number method proposed literature developed based certain assumption acquisition system assumption fulfilled quality fused product may seriously compromised propose postprocessing restoration strategy improve quality pansharpened image first stage introduce nonlocal variational model filtering chromatic component conditionally geometry panchromatic second stage structural component replaced locally histogrammatched panchromatic spatial enhancement exhaustive performance evaluation proposed restoration strategy illustrates 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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['Zhe Zhang', 'Shaoshan Liu', 'Grace Tsai', 'Hongbing Hu', 'Chen-Chi Chu', 'Feng Zheng']

1710.00893v1

In this paper, we present the PerceptIn Robotics Vision System (PIRVS) system, a visual-inertial computing hardware with embedded simultaneous localization and mapping (SLAM) algorithm. The PIRVS hardware is equipped with a multi-core processor, a global-shutter stereo camera, and an IMU with precise hardware synchronization. The PIRVS software features a novel and flexible sensor fusion approach to not only tightly integrate visual measurements with inertial measurements and also to loosely couple with additional sensor modalities. It runs in real-time on both PC and the PIRVS hardware. We perform a thorough evaluation of the proposed system using multiple public visual-inertial datasets. Experimental results demonstrate that our system reaches comparable accuracy of state-of-the-art visual-inertial algorithms on PC, while being more efficient on the PIRVS hardware.

PIRVS: An Advanced Visual-Inertial SLAM System with Flexible Sensor Fusion and Hardware Co-Design

http://arxiv.org/pdf/1710.00893v1

Title PIRVS Advanced VisualInertial SLAM System Flexible Sensor Fusion Hardware CoDesign Summary paper present PerceptIn Robotics Vision System PIRVS system visualinertial computing hardware embedded simultaneous localization mapping SLAM algorithm PIRVS hardware equipped multicore processor globalshutter stereo camera IMU precise hardware synchronization PIRVS software feature novel flexible sensor fusion approach tightly integrate visual measurement inertial measurement also loosely couple additional sensor modality run realtime PC PIRVS hardware perform thorough evaluation proposed system using multiple public visualinertial datasets Experimental result demonstrate system reach comparable accuracy stateoftheart visualinertial algorithm PC efficient PIRVS hardware 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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['Andy Zeng', 'Shuran Song', 'Kuan-Ting Yu', 'Elliott Donlon', 'Francois R. Hogan', 'Maria Bauza', 'Daolin Ma', 'Orion Taylor', 'Melody Liu', 'Eudald Romo', 'Nima Fazeli', 'Ferran Alet', 'Nikhil Chavan Dafle', 'Rachel Holladay', 'Isabella Morona', 'Prem Qu Nair', 'Druck Green', 'Ian Taylor', 'Weber Liu', 'Thomas Funkhouser', 'Alberto Rodriguez']

1710.01330v3

This paper presents a robotic pick-and-place system that is capable of grasping and recognizing both known and novel objects in cluttered environments. The key new feature of the system is that it handles a wide range of object categories without needing any task-specific training data for novel objects. To achieve this, it first uses a category-agnostic affordance prediction algorithm to select and execute among four different grasping primitive behaviors. It then recognizes picked objects with a cross-domain image classification framework that matches observed images to product images. Since product images are readily available for a wide range of objects (e.g., from the web), the system works out-of-the-box for novel objects without requiring any additional training data. Exhaustive experimental results demonstrate that our multi-affordance grasping achieves high success rates for a wide variety of objects in clutter, and our recognition algorithm achieves high accuracy for both known and novel grasped objects. The approach was part of the MIT-Princeton Team system that took 1st place in the stowing task at the 2017 Amazon Robotics Challenge. All code, datasets, and pre-trained models are available online at http://arc.cs.princeton.edu

Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching

http://arxiv.org/pdf/1710.01330v3

Title Robotic PickandPlace Novel Objects Clutter MultiAffordance Grasping CrossDomain Image Matching Summary paper present robotic pickandplace system capable grasping recognizing known novel object cluttered environment key new feature system handle wide range object category without needing taskspecific training data novel object achieve first us categoryagnostic affordance prediction algorithm select execute among four different grasping primitive behavior recognizes picked object crossdomain image classification framework match observed image product image Since product image readily available wide range object eg web system work outofthebox novel object without requiring additional training data Exhaustive experimental result demonstrate multiaffordance grasping achieves high success rate wide variety object clutter recognition algorithm achieves high accuracy known novel grasped object approach part MITPrinceton Team system took 1st place stowing task 2017 Amazon Robotics Challenge code datasets pretrained model available online httparccsprincetonedu 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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['Travis Gesslein', 'Daniel Scherer', 'Jens Grubert']

1710.01370v2

With the rising popularity of Augmented and Virtual Reality, there is a need for representing humans as virtual avatars in various application domains ranging from remote telepresence, games to medical applications. Besides explicitly modelling 3D avatars, sensing approaches that create person-specific avatars are becoming popular. However, affordable solutions typically suffer from a low visual quality and professional solution are often too expensive to be deployed in nonprofit projects. We present an open-source project, BodyDigitizer, which aims at providing both build instructions and configuration software for a high-resolution photogrammetry-based 3D body scanner. Our system encompasses up to 96 Rasperry PI cameras, active LED lighting, a sturdy frame construction and open-source configuration software. %We demonstrate the applicability of the body scanner in a nonprofit Mixed Reality health project. The detailed build instruction and software are available at http://www.bodydigitizer.org.

BodyDigitizer: An Open Source Photogrammetry-based 3D Body Scanner

http://arxiv.org/pdf/1710.01370v2

Title BodyDigitizer Open Source Photogrammetrybased 3D Body Scanner Summary rising popularity Augmented Virtual Reality need representing human virtual avatar various application domain ranging remote telepresence game medical application Besides explicitly modelling 3D avatar sensing approach create personspecific avatar becoming popular However affordable solution typically suffer low visual quality professional solution often expensive deployed nonprofit project present opensource project BodyDigitizer aim providing build instruction configuration software highresolution photogrammetrybased 3D body scanner system encompasses 96 Rasperry PI camera active LED lighting sturdy frame construction opensource configuration software demonstrate applicability body scanner nonprofit Mixed Reality health project detailed build instruction software available httpwwwbodydigitizerorg 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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['Nima Sedaghat', 'Ashish Mahabal']

1710.01422v1

Large sky surveys are increasingly relying on image subtraction pipelines for real-time (and archival) transient detection. In this process one has to contend with varying PSF, small brightness variations in many sources, as well as artifacts resulting from saturated stars, and, in general, matching errors. Very often the differencing is done with a reference image that is deeper than individual images and the attendant difference in noise characteristics can also lead to artifacts. We present here a deep-learning approach to transient detection that encapsulates all the steps of a traditional image subtraction pipeline -- image registration, background subtraction, noise removal, psf matching, and subtraction -- into a single real-time convolutional network. Once trained the method works lighteningly fast, and given that it does multiple steps at one go, the advantages for multi-CCD, fast surveys like ZTF and LSST are obvious.

Effective Image Differencing with ConvNets for Real-time Transient Hunting

http://arxiv.org/pdf/1710.01422v1

Title Effective Image Differencing ConvNets Realtime Transient Hunting Summary Large sky survey increasingly relying image subtraction pipeline realtime archival transient detection process one contend varying PSF small brightness variation many source well artifact resulting saturated star general matching error often differencing done reference image deeper individual image attendant difference noise characteristic also lead artifact present deeplearning approach transient detection encapsulates step traditional image subtraction pipeline image registration background subtraction noise removal psf matching subtraction single realtime convolutional network trained method work lighteningly fast given multiple step one go advantage multiCCD fast survey like ZTF LSST obvious 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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['Roopal Nahar', 'Akanksha Baranwal', 'K. Madhava Krishna']

1710.02260v1

Efficient and real time segmentation of color images has a variety of importance in many fields of computer vision such as image compression, medical imaging, mapping and autonomous navigation. Being one of the most computationally expensive operation, it is usually done through software imple- mentation using high-performance processors. In robotic systems, however, with the constrained platform dimensions and the need for portability, low power consumption and simultaneously the need for real time image segmentation, we envision hardware parallelism as the way forward to achieve higher acceleration. Field-programmable gate arrays (FPGAs) are among the best suited for this task as they provide high computing power in a small physical area. They exceed the computing speed of software based implementations by breaking the paradigm of sequential execution and accomplishing more per clock cycle operations by enabling hardware level parallelization at an architectural level. In this paper, we propose three novel architectures of a well known Efficient Graph based Image Segmentation algorithm. These proposed implementations optimizes time and power consumption when compared to software implementations. The hybrid design proposed, has notable furtherance of acceleration capabilities delivering atleast 2X speed gain over other implemen- tations, which henceforth allows real time image segmentation that can be deployed on Mobile Robotic systems.

FPGA based Parallelized Architecture of Efficient Graph based Image Segmentation Algorithm

http://arxiv.org/pdf/1710.02260v1

Title FPGA based Parallelized Architecture Efficient Graph based Image Segmentation Algorithm Summary Efficient real time segmentation color image variety importance many field computer vision image compression medical imaging mapping autonomous navigation one computationally expensive operation usually done software imple mentation using highperformance processor robotic system however constrained platform dimension need portability low power consumption simultaneously need real time image segmentation envision hardware parallelism way forward achieve higher acceleration Fieldprogrammable gate array FPGAs among best suited task provide high computing power small physical area exceed computing speed software based implementation breaking paradigm sequential execution accomplishing per clock cycle operation enabling hardware level parallelization architectural level paper propose three novel architecture well known Efficient Graph based Image Segmentation algorithm proposed implementation optimizes time power consumption compared software implementation hybrid design proposed notable furtherance acceleration capability delivering atleast 2X speed gain implemen tations henceforth allows real time image segmentation deployed Mobile Robotic 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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['Jean Stawiaski']

1710.02316v1

This article presents a multiscale patch based convolutional neural network for the automatic segmentation of brain tumors in multi-modality 3D MR images. We use multiscale deep supervision and inputs to train a convolutional network. We evaluate the effectiveness of the proposed approach on the BRATS 2017 segmentation challenge where we obtained dice scores of 0.755, 0.900, 0.782 and 95% Hausdorff distance of 3.63mm, 4.10mm, and 6.81mm for enhanced tumor core, whole tumor and tumor core respectively.

A Multiscale Patch Based Convolutional Network for Brain Tumor Segmentation

http://arxiv.org/pdf/1710.02316v1

Title Multiscale Patch Based Convolutional Network Brain Tumor Segmentation Summary article present multiscale patch based convolutional neural network automatic segmentation brain tumor multimodality 3D MR image use multiscale deep supervision input train convolutional network evaluate effectiveness proposed approach BRATS 2017 segmentation challenge obtained dice score 0755 0900 0782 95 Hausdorff distance 363mm 410mm 681mm enhanced tumor core whole tumor tumor core respectively 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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['Haresh Karnan', 'Aritra Biswas', 'Pranav Vaidik Dhulipala', 'Jan Dufek', 'Robin Murphy']

1710.02932v1

This paper presents an algorithm and the implementation of a motor schema to aid the visual localization subsystem of the ongoing EMILY project at Texas A and M University. The EMILY project aims to team an Unmanned Surface Vehicle (USV) with an Unmanned Aerial Vehicle (UAV) to augment the search and rescue of marine casualties during an emergency response phase. The USV is designed to serve as a flotation device once it reaches the victims. A live video feed from the UAV is provided to the casuality responders giving them a visual estimate of the USVs orientation and position to help with its navigation. One of the challenges involved with casualty response using a USV UAV team is to simultaneously control the USV and track it. In this paper, we present an implemented solution to automate the UAV camera movements to keep the USV in view at all times. The motor schema proposed, uses the USVs coordinates from the visual localization subsystem to control the UAVs camera movements and track the USV with minimal camera movements such that the USV is always in the cameras field of view.

Visual Servoing of Unmanned Surface Vehicle from Small Tethered Unmanned Aerial Vehicle

http://arxiv.org/pdf/1710.02932v1

Title Visual Servoing Unmanned Surface Vehicle Small Tethered Unmanned Aerial Vehicle Summary paper present algorithm implementation motor schema aid visual localization subsystem ongoing EMILY project Texas University EMILY project aim team Unmanned Surface Vehicle USV Unmanned Aerial Vehicle UAV augment search rescue marine casualty emergency response phase USV designed serve flotation device reach victim live video feed UAV provided casuality responder giving visual estimate USVs orientation position help navigation One challenge involved casualty response using USV UAV team simultaneously control USV track paper present implemented solution automate UAV camera movement keep USV view time motor schema proposed us USVs coordinate visual localization subsystem control UAVs camera movement track USV minimal camera movement USV always camera field view 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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['Alexander Hann', 'Lucas Bettac', 'Mark M. Haenle', 'Tilmann Graeter', 'Andreas W. Berger', 'Jens Dreyhaupt', 'Dieter Schmalstieg', 'Wolfram G. Zoller', 'Jan Egger']

1710.02984v1

Manual segmentation of hepatic metastases in ultrasound images acquired from patients suffering from pancreatic cancer is common practice. Semiautomatic measurements promising assistance in this process are often assessed using a small number of lesions performed by examiners who already know the algorithm. In this work, we present the application of an algorithm for the segmentation of liver metastases due to pancreatic cancer using a set of 105 different images of metastases. The algorithm and the two examiners had never assessed the images before. The examiners first performed a manual segmentation and, after five weeks, a semiautomatic segmentation using the algorithm. They were satisfied in up to 90% of the cases with the semiautomatic segmentation results. Using the algorithm was significantly faster and resulted in a median Dice similarity score of over 80%. Estimation of the inter-operator variability by using the intra class correlation coefficient was good with 0.8. In conclusion, the algorithm facilitates fast and accurate segmentation of liver metastases, comparable to the current gold standard of manual segmentation.

Algorithm guided outlining of 105 pancreatic cancer liver metastases in Ultrasound

http://arxiv.org/pdf/1710.02984v1

Title Algorithm guided outlining 105 pancreatic cancer liver metastasis Ultrasound Summary Manual segmentation hepatic metastasis ultrasound image acquired patient suffering pancreatic cancer common practice Semiautomatic measurement promising assistance process often assessed using small number lesion performed examiner already know algorithm work present application algorithm segmentation liver metastasis due pancreatic cancer using set 105 different image metastasis algorithm two examiner never assessed image examiner first performed manual segmentation five week semiautomatic segmentation using algorithm satisfied 90 case semiautomatic segmentation result Using algorithm significantly faster resulted median Dice similarity score 80 Estimation interoperator variability using intra class correlation coefficient good 08 conclusion algorithm facilitates fast accurate segmentation liver metastasis comparable current gold standard manual 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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['Xiao Xiang Zhu', 'Devis Tuia', 'Lichao Mou', 'Gui-Song Xia', 'Liangpei Zhang', 'Feng Xu', 'Friedrich Fraundorfer']

1710.03959v1

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.

Deep learning in remote sensing: a review

http://arxiv.org/pdf/1710.03959v1

Title Deep learning remote sensing review Summary Standing paradigm shift towards dataintensive science machine learning technique becoming increasingly important particular major breakthrough field deep learning proven extremely powerful tool many field Shall embrace deep learning key resist blackbox solution controversial opinion remote sensing community article analyze challenge using deep learning remote sensing data analysis review recent advance provide resource make deep learning remote sensing ridiculously simple start importantly advocate remote sensing scientist bring expertise deep learning use implicit general model tackle unprecedented largescale influential challenge climate change urbanization 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 T. McCann', 'Kyong Hwan Jin', 'Michael Unser']

1710.04011v1

In this survey paper, we review recent uses of convolution neural networks (CNNs) to solve inverse problems in imaging. It has recently become feasible to train deep CNNs on large databases of images, and they have shown outstanding performance on object classification and segmentation tasks. Motivated by these successes, researchers have begun to apply CNNs to the resolution of inverse problems such as denoising, deconvolution, super-resolution, and medical image reconstruction, and they have started to report improvements over state-of-the-art methods, including sparsity-based techniques such as compressed sensing. Here, we review the recent experimental work in these areas, with a focus on the critical design decisions: Where does the training data come from? What is the architecture of the CNN? and How is the learning problem formulated and solved? We also bring together a few key theoretical papers that offer perspective on why CNNs are appropriate for inverse problems and point to some next steps in the field.

A Review of Convolutional Neural Networks for Inverse Problems in Imaging

http://arxiv.org/pdf/1710.04011v1

Title Review Convolutional Neural Networks Inverse Problems Imaging Summary survey paper review recent us convolution neural network CNNs solve inverse problem imaging recently become feasible train deep CNNs large database image shown outstanding performance object classification segmentation task Motivated success researcher begun apply CNNs resolution inverse problem denoising deconvolution superresolution medical image reconstruction started report improvement stateoftheart method including sparsitybased technique compressed sensing review recent experimental work area focus critical design decision training data come architecture CNN learning problem formulated solved also bring together key theoretical paper offer perspective CNNs appropriate inverse problem point next step field 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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['Chun Pong Lau', 'Chun Pang Yung', 'Lok Ming Lui']

1710.04034v1

Image retargeting aims to resize an image to one with a prescribed aspect ratio. Simple scaling inevitably introduces unnatural geometric distortions on the important content of the image. In this paper, we propose a simple and yet effective method to resize an image, which preserves the geometry of the important content, using the Beltrami representation. Our algorithm allows users to interactively label content regions as well as line structures. Image resizing can then be achieved by warping the image by an orientation-preserving bijective warping map with controlled distortion. The warping map is represented by its Beltrami representation, which captures the local geometric distortion of the map. By carefully prescribing the values of the Beltrami representation, images with different complexity can be effectively resized. Our method does not require solving any optimization problems and tuning parameters throughout the process. This results in a simple and efficient algorithm to solve the image retargeting problem. Extensive experiments have been carried out, which demonstrate the efficacy of our proposed method.

Image retargeting via Beltrami representation

http://arxiv.org/pdf/1710.04034v1

Title Image retargeting via Beltrami representation Summary Image retargeting aim resize image one prescribed aspect ratio Simple scaling inevitably introduces unnatural geometric distortion important content image paper propose simple yet effective method resize image preserve geometry important content using Beltrami representation algorithm allows user interactively label content region well line structure Image resizing achieved warping image orientationpreserving bijective warping map controlled distortion warping map represented Beltrami representation capture local geometric distortion map carefully prescribing value Beltrami representation image different complexity effectively resized method require solving optimization problem tuning parameter throughout process result simple efficient algorithm solve image retargeting problem Extensive experiment carried demonstrate efficacy proposed 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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['Enrico Celeghini']

1710.04207v1

We propose an approach to image processing related to algebraic operators acting in the space of images. In view of the interest in the applications in optics and computer science, mathematical aspects of the paper have been simplified as much as possible. Underlying theory, related to rigged Hilbert spaces and Lie algebras, is discussed elsewhere

Algebraic Image Processing

http://arxiv.org/pdf/1710.04207v1

Title Algebraic Image Processing Summary propose approach image processing related algebraic operator acting space image view interest application optic computer science mathematical aspect paper simplified much possible Underlying theory related rigged Hilbert space Lie algebra discussed elsewhere 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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['David Casillas-Perez', 'Daniel Pizarro']

1710.04265v1

The article proves the existence of a maximum of two possible solutions to the initial value problem composed by the planar-perspective equation and an initial condition. This initial value problem has a geometric interpretation. Solutions are curves than pass trough the initial condition which is a point of the plane.

Solutions of Quadratic First-Order ODEs applied to Computer Vision Problems

http://arxiv.org/pdf/1710.04265v1

Title Solutions Quadratic FirstOrder ODEs applied Computer Vision Problems Summary article prof existence maximum two possible solution initial value problem composed planarperspective equation initial condition initial value problem geometric interpretation Solutions curve pas trough initial condition point plane 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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['Peihan Tu']

1710.04374v1

Digital image correlation (DIC) is a widely used optical metrology for surface deformation measurements. DIC relies on nonlinear optimization method. Thus an initial guess is quite important due to its influence on the converge characteristics of the algorithm. In order to obtain a reliable, accurate initial guess, a reliability-guided digital image correlation (RG-DIC) method, which is able to intelligently obtain a reliable initial guess without using time-consuming integer-pixel registration, was proposed. However, the RG-DIC and its improved methods are path-dependent and cannot be fully parallelized. Besides, it is highly possible that RG-DIC fails in the full-field analysis of deformation without manual intervention if the deformation fields contain large areas of discontinuous deformation. Feature-based initial guess is highly robust while it is relatively time-consuming. Recently, path-independent algorithm, fast Fourier transform-based cross correlation (FFT-CC) algorithm, was proposed to estimate the initial guess. Complete parallelizability is the major advantage of the FFT-CC algorithm, while it is sensitive to small deformation. Wu et al proposed an efficient integer-pixel search scheme, but the parameters of this algorithm are set by the users empirically. In this technical note, a fully parallelizable DIC method is proposed. Different from RG-DIC method, the proposed method divides DIC algorithm into two parts: full-field initial guess estimation and sub-pixel registration. The proposed method has the following benefits: 1) providing a pre-knowledge of deformation fields; 2) saving computational time; 3) reducing error propagation; 4) integratability with well-established DIC algorithms; 5) fully parallelizability.

Fast, Accurate and Fully Parallelizable Digital Image Correlation

http://arxiv.org/pdf/1710.04374v1

Title Fast Accurate Fully Parallelizable Digital Image Correlation Summary Digital image correlation DIC widely used optical metrology surface deformation measurement DIC relies nonlinear optimization method Thus initial guess quite important due influence converge characteristic algorithm order obtain reliable accurate initial guess reliabilityguided digital image correlation RGDIC method able intelligently obtain reliable initial guess without using timeconsuming integerpixel registration proposed However RGDIC improved method pathdependent cannot fully parallelized Besides highly possible RGDIC fails fullfield analysis deformation without manual intervention deformation field contain large area discontinuous deformation Featurebased initial guess highly robust relatively timeconsuming Recently pathindependent algorithm fast Fourier transformbased cross correlation FFTCC algorithm proposed estimate initial guess Complete parallelizability major advantage FFTCC algorithm sensitive small deformation Wu et al proposed efficient integerpixel search scheme parameter algorithm set user empirically technical note fully parallelizable DIC method proposed Different RGDIC method proposed method divide DIC algorithm two part fullfield initial guess estimation subpixel registration proposed method following benefit 1 providing preknowledge deformation field 2 saving computational time 3 reducing error propagation 4 integratability wellestablished DIC algorithm 5 fully parallelizability 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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['Eman Alibeigi', 'Shadrokh Samavi', 'Shahram Shirani', 'Zahra Rahmani']

1710.05027v1

Fingerprint verification is an important bio-metric technique for personal identification. Most of the automatic verification systems are based on matching of fingerprint minutiae. Extraction of minutiae is an essential process which requires estimation of orientation of the lines in an image. Most of the existing methods involve intense mathematical computations and hence are performed through software means. In this paper a hardware scheme to perform real time orientation estimation is presented which is based on pipelined architecture. Synthesized circuits proved the functionality and accuracy of the suggested method.

Real time ridge orientation estimation for fingerprint images

http://arxiv.org/pdf/1710.05027v1

Title Real time ridge orientation estimation fingerprint image Summary Fingerprint verification important biometric technique personal identification automatic verification system based matching fingerprint minutia Extraction minutia essential process requires estimation orientation line image existing method involve intense mathematical computation hence performed software mean paper hardware scheme perform real time orientation estimation presented based pipelined architecture Synthesized circuit proved functionality accuracy suggested 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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['Bin Li', 'Hu Luo', 'Haoxin Zhang', 'Shunquan Tan', 'Zhongzhou Ji']

1710.05477v1

Detection of double JPEG compression is important to forensics analysis. A few methods were proposed based on convolutional neural networks (CNNs). These methods only accept inputs from pre-processed data, such as histogram features and/or decompressed images. In this paper, we present a CNN solution by using raw DCT (discrete cosine transformation) coefficients from JPEG images as input. Considering the DCT sub-band nature in JPEG, a multiple-branch CNN structure has been designed to reveal whether a JPEG format image has been doubly compressed. Comparing with previous methods, the proposed method provides end-to-end detection capability. Extensive experiments have been carried out to demonstrate the effectiveness of the proposed network.

A multi-branch convolutional neural network for detecting double JPEG compression

http://arxiv.org/pdf/1710.05477v1

Title multibranch convolutional neural network detecting double JPEG compression Summary Detection double JPEG compression important forensics analysis method proposed based convolutional neural network CNNs method accept input preprocessed data histogram feature andor decompressed image paper present CNN solution using raw DCT discrete cosine transformation coefficient JPEG image input Considering DCT subband nature JPEG multiplebranch CNN structure designed reveal whether JPEG format image doubly compressed Comparing previous method proposed method provides endtoend detection capability Extensive experiment carried demonstrate effectiveness proposed 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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['İlker Bozcan', 'Yağmur Oymak', 'İdil Zeynep Alemdar', 'Sinan Kalkan']

1710.05664v1

Scene models allow robots to reason about what is in the scene, what else should be in it, and what should not be in it. In this paper, we propose a hybrid Boltzmann Machine (BM) for scene modeling where relations between objects are integrated. To be able to do that, we extend BM to include tri-way edges between visible (object) nodes and make the network to share the relations across different objects. We evaluate our method against several baseline models (Deep Boltzmann Machines, and Restricted Boltzmann Machines) on a scene classification dataset, and show that it performs better in several scene reasoning tasks.

What is (missing or wrong) in the scene? A Hybrid Deep Boltzmann Machine For Contextualized Scene Modeling

http://arxiv.org/pdf/1710.05664v1

Title missing wrong scene Hybrid Deep Boltzmann Machine Contextualized Scene Modeling Summary Scene model allow robot reason scene else paper propose hybrid Boltzmann Machine BM scene modeling relation object integrated able extend BM include triway edge visible object node make network share relation across different object evaluate method several baseline model Deep Boltzmann Machines Restricted Boltzmann Machines scene classification dataset show performs better several scene reasoning 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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['Scott Allen Burns']

1710.05732v2

The color sensation evoked by an object depends on both the spectral power distribution of the illumination and the reflectance properties of the object being illuminated. The color sensation can be characterized by three color-space values, such as XYZ, RGB, HSV, L*a*b*, etc. It is straightforward to compute the three values given the illuminant and reflectance curves. The converse process of computing a reflectance curve given the color-space values and the illuminant is complicated by the fact that an infinite number of different reflectance curves can give rise to a single set of color-space values (metamerism). This paper presents five algorithms for generating a reflectance curve from a specified sRGB triplet, written for a general audience. The algorithms are designed to generate reflectance curves that are similar to those found with naturally occurring colored objects. The computed reflectance curves are compared to a database of thousands of reflectance curves measured from paints and pigments available both commercially and in nature, and the similarity is quantified. One particularly useful application of these algorithms is in the field of computer graphics, where modeling color transformations sometimes requires wavelength-specific information, such as when modeling subtractive color mixture.

Generating Reflectance Curves from sRGB Triplets

http://arxiv.org/pdf/1710.05732v2

Title Generating Reflectance Curves sRGB Triplets Summary color sensation evoked object depends spectral power distribution illumination reflectance property object illuminated color sensation characterized three colorspace value XYZ RGB HSV Lab etc straightforward compute three value given illuminant reflectance curve converse process computing reflectance curve given colorspace value illuminant complicated fact infinite number different reflectance curve give rise single set colorspace value metamerism paper present five algorithm generating reflectance curve specified sRGB triplet written general audience algorithm designed generate reflectance curve similar found naturally occurring colored object computed reflectance curve compared database thousand reflectance curve measured paint pigment available commercially nature similarity quantified One particularly useful application algorithm field computer graphic modeling color transformation sometimes requires wavelengthspecific information modeling subtractive color mixture 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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['Farshad Kheiri', 'Shadrokh Samavi', 'Nader Karimi']

1710.05749v1

Two critical steps in fingerprint recognition are binarization and thinning of the image. The need for real time processing motivates us to select local adaptive thresholding approach for the binarization step. We introduce a new hardware for this purpose based on pipeline architecture. We propose a formula for selecting an optimal block size for the thresholding purpose. To decrease minutiae false detection, the binarized image is dilated. We also present in this paper a new pipeline structure for implementing the thinning algorithm

Hardware design for binarization and thinning of fingerprint images

http://arxiv.org/pdf/1710.05749v1

Title Hardware design binarization thinning fingerprint image Summary Two critical step fingerprint recognition binarization thinning image need real time processing motivates u select local adaptive thresholding approach binarization step introduce new hardware purpose based pipeline architecture propose formula selecting optimal block size thresholding purpose decrease minutia false detection binarized image dilated also present paper new pipeline structure implementing thinning 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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['Leonid P. Yaroslavsky']

1710.05985v2

The problem of minimization of the number of measurements needed for digital image acquisition and reconstruction with a given accuracy is addressed. Basics of the sampling theory are outlined to show that the lower bound of signal sampling rate sufficient for signal reconstruction with a given accuracy is equal to the spectrum sparsity of the signal sparse approximation that has this accuracy. It is revealed that the compressed sensing approach, which was advanced as a solution to the sampling rate minimization problem, is far from reaching the sampling rate theoretical minimum. Potentials and limitations of compressed sensing are demystified using a simple and intutive model, A method of image Arbitrary Sampling and Bounded Spectrum Reconstruction (ASBSR-method) is described that allows to draw near the image sampling rate theoretical minimum. Presented and discussed are also results of experimental verification of the ASBSR-method and its possible applicability extensions to solving various underdetermined inverse problems such as color image demosaicing, image in-painting, image reconstruction from their sparsely sampled or decimated projections, image reconstruction from the modulus of its Fourier spectrum, and image reconstruction from its sparse samples in Fourier domain

Compressed Sensing, ASBSR-method of image sampling and reconstruction and the problem of digital image acquisition with the lowest possible sampling rate

http://arxiv.org/pdf/1710.05985v2

Title Compressed Sensing ASBSRmethod image sampling reconstruction problem digital image acquisition lowest possible sampling rate Summary problem minimization number measurement needed digital image acquisition reconstruction given accuracy addressed Basics sampling theory outlined show lower bound signal sampling rate sufficient signal reconstruction given accuracy equal spectrum sparsity signal sparse approximation accuracy revealed compressed sensing approach advanced solution sampling rate minimization problem far reaching sampling rate theoretical minimum Potentials limitation compressed sensing demystified using simple intutive model method image Arbitrary Sampling Bounded Spectrum Reconstruction ASBSRmethod described allows draw near image sampling rate theoretical minimum Presented discussed also result experimental verification ASBSRmethod possible applicability extension solving various underdetermined inverse problem color image demosaicing image inpainting image reconstruction sparsely sampled decimated projection image reconstruction modulus Fourier spectrum image reconstruction sparse sample Fourier 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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['Da Chen', 'Laurent D. Cohen']

1710.06194v1

In this paper, we propose a new minimal path model for minimally interactive retinal vessel centerline extraction. The main contribution lies at the construction of a novel coherence-penalized Riemannian metric in a lifted space, dependently of the local geometry of tubularity and an external scalar-valued reference feature map. The globally minimizing curves associated to the proposed metric favour to pass through a set of retinal vessel segments with low variations of the feature map, thus can avoid the short branches combination problem and shortcut problem, commonly suffered by the existing minimal path models in the application of retinal imaging. We validate our model on a series of retinal vessel patches obtained from the DRIVE and IOSTAR datasets, showing that our model indeed get promising results.

A New Coherence-Penalized Minimal Path Model with Application to Retinal Vessel Centerline Delineation

http://arxiv.org/pdf/1710.06194v1

Title New CoherencePenalized Minimal Path Model Application Retinal Vessel Centerline Delineation Summary paper propose new minimal path model minimally interactive retinal vessel centerline extraction main contribution lie construction novel coherencepenalized Riemannian metric lifted space dependently local geometry tubularity external scalarvalued reference feature map globally minimizing curve associated proposed metric favour pas set retinal vessel segment low variation feature map thus avoid short branch combination problem shortcut problem commonly suffered existing minimal path model application retinal imaging validate model series retinal vessel patch obtained DRIVE IOSTAR datasets showing model indeed get promising result 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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['Atsushi Ito', 'Makoto Miura', 'Kazushi Ueda']

1710.06205v1

We discuss the geometry of rational maps from a projective space of an arbitrary dimension to the product of projective spaces of lower dimensions induced by linear projections. In particular, we give a purely algebro-geometric proof of the projective reconstruction theorem by Hartley and Schaffalitzky [HS09].

Projective reconstruction in algebraic vision

http://arxiv.org/pdf/1710.06205v1

Title Projective reconstruction algebraic vision Summary discus geometry rational map projective space arbitrary dimension product projective space lower dimension induced linear projection particular give purely algebrogeometric proof projective reconstruction theorem Hartley Schaffalitzky HS09 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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['Varuna De Silva', 'Jamie Roche', 'Ahmet Kondoz']

1710.06230v1

Driverless vehicles operate by sensing and perceiving its surrounding environment to make the accurate driving decisions. A combination of several different sensors such as LiDAR, radar, ultrasound sensors and cameras are utilized to sense the surrounding environment of driverless vehicles. The heterogeneous sensors simultaneously capture various physical attributes of the environment. Such multimodality and redundancy of sensing need to be positively utilized for reliable and consistent perception of the environment through sensor data fusion. However, these multimodal sensor data streams are different from each other in many ways, such as temporal and spatial resolution, data format, and geometric alignment. For the subsequent perception algorithms to utilize the diversity offered by multimodal sensing, the data streams need to be spatially, geometrically and temporally aligned with each other. In this paper, we address the problem of fusing the outputs of a Light Detection and Ranging (LiDAR) scanner and a wide-angle monocular image sensor. The outputs of LiDAR scanner and the image sensor are of different spatial resolutions and need to be aligned with each other. A geometrical model is used to spatially align the two sensor outputs, followed by a Gaussian Process (GP) regression based resolution matching algorithm to interpolate the missing data with quantifiable uncertainty. The results indicate that the proposed sensor data fusion framework significantly aids the subsequent perception steps, as illustrated by the performance improvement of a typical free space detection algorithm.

Fusion of LiDAR and Camera Sensor Data for Environment Sensing in Driverless Vehicles

http://arxiv.org/pdf/1710.06230v1

Title Fusion LiDAR Camera Sensor Data Environment Sensing Driverless Vehicles Summary Driverless vehicle operate sensing perceiving surrounding environment make accurate driving decision combination several different sensor LiDAR radar ultrasound sensor camera utilized sense surrounding environment driverless vehicle heterogeneous sensor simultaneously capture various physical attribute environment multimodality redundancy sensing need positively utilized reliable consistent perception environment sensor data fusion However multimodal sensor data stream different many way temporal spatial resolution data format geometric alignment subsequent perception algorithm utilize diversity offered multimodal sensing data stream need spatially geometrically temporally aligned paper address problem fusing output Light Detection Ranging LiDAR scanner wideangle monocular image sensor output LiDAR scanner image sensor different spatial resolution need aligned geometrical model used spatially align two sensor output followed Gaussian Process GP regression based resolution matching algorithm interpolate missing data quantifiable uncertainty result indicate proposed sensor data fusion framework significantly aid subsequent perception step illustrated performance improvement typical free space detection 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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['Wim Abbeloos', 'Esra Ataer-Cansizoglu', 'Sergio Caccamo', 'Yuichi Taguchi', 'Yukiyasu Domae']

1710.06231v1

Unsupervised object modeling is important in robotics, especially for handling a large set of objects. We present a method for unsupervised 3D object discovery, reconstruction, and localization that exploits multiple instances of an identical object contained in a single RGB-D image. The proposed method does not rely on segmentation, scene knowledge, or user input, and thus is easily scalable. Our method aims to find recurrent patterns in a single RGB-D image by utilizing appearance and geometry of the salient regions. We extract keypoints and match them in pairs based on their descriptors. We then generate triplets of the keypoints matching with each other using several geometric criteria to minimize false matches. The relative poses of the matched triplets are computed and clustered to discover sets of triplet pairs with similar relative poses. Triplets belonging to the same set are likely to belong to the same object and are used to construct an initial object model. Detection of remaining instances with the initial object model using RANSAC allows to further expand and refine the model. The automatically generated object models are both compact and descriptive. We show quantitative and qualitative results on RGB-D images with various objects including some from the Amazon Picking Challenge. We also demonstrate the use of our method in an object picking scenario with a robotic arm.

3D Object Discovery and Modeling Using Single RGB-D Images Containing Multiple Object Instances

http://arxiv.org/pdf/1710.06231v1

Title 3D Object Discovery Modeling Using Single RGBD Images Containing Multiple Object Instances Summary Unsupervised object modeling important robotics especially handling large set object present method unsupervised 3D object discovery reconstruction localization exploit multiple instance identical object contained single RGBD image proposed method rely segmentation scene knowledge user input thus easily scalable method aim find recurrent pattern single RGBD image utilizing appearance geometry salient region extract keypoints match pair based descriptor generate triplet keypoints matching using several geometric criterion minimize false match relative pose matched triplet computed clustered discover set triplet pair similar relative pose Triplets belonging set likely belong object used construct initial object model Detection remaining instance initial object model using RANSAC allows expand refine model automatically generated object model compact descriptive show quantitative qualitative result RGBD image various object including Amazon Picking Challenge also demonstrate use method object picking scenario robotic arm 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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['Ertugrul Bayraktar', 'Pinar Boyraz']

1710.06232v1

The purpose of this study is to provide a detailed performance comparison of feature detector/descriptor methods, particularly when their various combinations are used for image-matching. The localization experiments of a mobile robot in an indoor environment are presented as a case study. In these experiments, 3090 query images and 127 dataset images were used. This study includes five methods for feature detectors (features from accelerated segment test (FAST), oriented FAST and rotated binary robust independent elementary features (BRIEF) (ORB), speeded-up robust features (SURF), scale invariant feature transform (SIFT), and binary robust invariant scalable keypoints (BRISK)) and five other methods for feature descriptors (BRIEF, BRISK, SIFT, SURF, and ORB). These methods were used in 23 different combinations and it was possible to obtain meaningful and consistent comparison results using the performance criteria defined in this study. All of these methods were used independently and separately from each other as either feature detector or descriptor. The performance analysis shows the discriminative power of various combinations of detector and descriptor methods. The analysis is completed using five parameters: (i) accuracy, (ii) time, (iii) angle difference between keypoints, (iv) number of correct matches, and (v) distance between correctly matched keypoints. In a range of 60{\deg}, covering five rotational pose points for our system, the FAST-SURF combination had the lowest distance and angle difference values and the highest number of matched keypoints. SIFT-SURF was the most accurate combination with a 98.41% correct classification rate. The fastest algorithm was ORB-BRIEF, with a total running time of 21,303.30 s to match 560 images captured during motion with 127 dataset images.

Analysis of feature detector and descriptor combinations with a localization experiment for various performance metrics

http://arxiv.org/pdf/1710.06232v1

Title Analysis feature detector descriptor combination localization experiment various performance metric Summary purpose study provide detailed performance comparison feature detectordescriptor method particularly various combination used imagematching localization experiment mobile robot indoor environment presented case study experiment 3090 query image 127 dataset image used study includes five method feature detector feature accelerated segment test FAST oriented FAST rotated binary robust independent elementary feature BRIEF ORB speededup robust feature SURF scale invariant feature transform SIFT binary robust invariant scalable keypoints BRISK five method feature descriptor BRIEF BRISK SIFT SURF ORB method used 23 different combination possible obtain meaningful consistent comparison result using performance criterion defined study method used independently separately either feature detector descriptor performance analysis show discriminative power various combination detector descriptor method analysis completed using five parameter accuracy ii time iii angle difference keypoints iv number correct match v distance correctly matched keypoints range 60deg covering five rotational pose point system FASTSURF combination lowest distance angle difference value highest number matched keypoints SIFTSURF accurate combination 9841 correct classification rate fastest algorithm ORBBRIEF total running time 2130330 match 560 image captured motion 127 dataset image 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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['Marco Carraro', 'Matteo Munaro', 'Jeff Burke', 'Emanuele Menegatti']

1710.06235v1

This paper proposes a novel system to estimate and track the 3D poses of multiple persons in calibrated RGB-Depth camera networks. The multi-view 3D pose of each person is computed by a central node which receives the single-view outcomes from each camera of the network. Each single-view outcome is computed by using a CNN for 2D pose estimation and extending the resulting skeletons to 3D by means of the sensor depth. The proposed system is marker-less, multi-person, independent of background and does not make any assumption on people appearance and initial pose. The system provides real-time outcomes, thus being perfectly suited for applications requiring user interaction. Experimental results show the effectiveness of this work with respect to a baseline multi-view approach in different scenarios. To foster research and applications based on this work, we released the source code in OpenPTrack, an open source project for RGB-D people tracking.

Real-time marker-less multi-person 3D pose estimation in RGB-Depth camera networks

http://arxiv.org/pdf/1710.06235v1

Title Realtime markerless multiperson 3D pose estimation RGBDepth camera network Summary paper proposes novel system estimate track 3D pose multiple person calibrated RGBDepth camera network multiview 3D pose person computed central node receives singleview outcome camera network singleview outcome computed using CNN 2D pose estimation extending resulting skeleton 3D mean sensor depth proposed system markerless multiperson independent background make assumption people appearance initial pose system provides realtime outcome thus perfectly suited application requiring user interaction Experimental result show effectiveness work respect baseline multiview approach different scenario foster research application based work released source code OpenPTrack open source project RGBD people tracking 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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['Zhiyu Sun', 'Yusen He', 'Andrey Gritsenko', 'Amaury Lendasse', 'Stephen Baek']

1710.06368v1

A robust and informative local shape descriptor plays an important role in mesh registration. In this regard, spectral descriptors that are based on the spectrum of the Laplace-Beltrami operator have gained a spotlight among the researchers for the last decade due to their desirable properties, such as isometry invariance. Despite such, however, spectral descriptors often fail to give a correct similarity measure for non-isometric cases where the metric distortion between the models is large. Hence, they are in general not suitable for the registration problems, except for the special cases when the models are near-isometry. In this paper, we investigate a way to develop shape descriptors for non-isometric registration tasks by embedding the spectral shape descriptors into a different metric space where the Euclidean distance between the elements directly indicates the geometric dissimilarity. We design and train a Siamese deep neural network to find such an embedding, where the embedded descriptors are promoted to rearrange based on the geometric similarity. We found our approach can significantly enhance the performance of the conventional spectral descriptors for the non-isometric registration tasks, and outperforms recent state-of-the-art method reported in literature.

Deep Spectral Descriptors: Learning the point-wise correspondence metric via Siamese deep neural networks

http://arxiv.org/pdf/1710.06368v1

Title Deep Spectral Descriptors Learning pointwise correspondence metric via Siamese deep neural network Summary robust informative local shape descriptor play important role mesh registration regard spectral descriptor based spectrum LaplaceBeltrami operator gained spotlight among researcher last decade due desirable property isometry invariance Despite however spectral descriptor often fail give correct similarity measure nonisometric case metric distortion model large Hence general suitable registration problem except special case model nearisometry paper investigate way develop shape descriptor nonisometric registration task embedding spectral shape descriptor different metric space Euclidean distance element directly indicates geometric dissimilarity design train Siamese deep neural network find embedding embedded descriptor promoted rearrange based geometric similarity found approach significantly enhance performance conventional spectral descriptor nonisometric registration task outperforms recent stateoftheart method reported 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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['Ashraf Qadir', 'Jeremiah Neubert']

1710.06495v1

In this work we present a unified method of relative camera pose estimation from points and lines correspondences. Given a set of 2D points and lines correspondences in three views, of which two are known, a method has been developed for estimating the camera pose of the third view. Novelty of this algorithm is to combine both points and lines correspondences in the camera pose estimation which enables us to compute relative camera pose with a small number of feature correspondences. Our central idea is to exploit the tri-linear relationship between three views and generate a set of linear equations from the points and lines correspondences in the three views. The desired solution to the system of equations are expressed as a linear combination of the singular vectors and the coefficients are computed by solving a small set of quadratic equations generated by imposing orthonormality constraints for general camera motion. The advantages of the proposed method are demonstrated by experimenting on publicly available data set. Results show the robustness and efficiency of the method in relative camera pose estimation for both small and large camera motion with a small set of points and line features.

A Line-Point Unified Solution to Relative Camera Pose Estimation

http://arxiv.org/pdf/1710.06495v1

Title LinePoint Unified Solution Relative Camera Pose Estimation Summary work present unified method relative camera pose estimation point line correspondence Given set 2D point line correspondence three view two known method developed estimating camera pose third view Novelty algorithm combine point line correspondence camera pose estimation enables u compute relative camera pose small number feature correspondence central idea exploit trilinear relationship three view generate set linear equation point line correspondence three view desired solution system equation expressed linear combination singular vector coefficient computed solving small set quadratic equation generated imposing orthonormality constraint general camera motion advantage proposed method demonstrated experimenting publicly available data set Results show robustness efficiency method relative camera pose estimation small large camera motion small set point line 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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['Michel Conrado Cardoso Meneses']

1710.06518v1

Autonomous robots are used as the tool to solve many kinds of problems, such as environmental mapping and monitoring. Either for adverse conditions related to the human presence or even for the need to reduce costs, it is certain that many efforts have been made to develop robots with an increasingly high level of autonomy. They must be capable of locomotion through dynamic environments, without human operators or assistant systems' help. It is noted, thus, that the form of perception and modeling of the environment becomes significantly relevant to navigation. Among the main sensing methods are those based on vision. Through this, it is possible to create highly-detailed models about the environment, since many characteristics can be measured, such as texture, color, and illumination. However, the most accurate vision-based navigation techniques are computationally expensive to run on low-cost mobile platforms. Therefore, the goal of this work was to develop a low-cost robot, controlled by a Raspberry Pi, whose navigation system is based on vision. For this purpose, the strategy used consisted in identifying obstacles via optical flow pattern recognition. Through this signal, it is possible to infer the relative displacement between the robot and other elements in the environment. Its estimation was done using the Lucas-Kanade algorithm, which can be executed by the Raspberry Pi without harming its performance. Finally, an SVM based classifier was used to identify patterns of this signal associated with obstacles movement. The developed system was evaluated considering its execution over an optical flow pattern dataset extracted from a real navigation environment. In the end, it was verified that the processing frequency of the system was superior to the others. Furthermore, its accuracy and acquisition cost were, respectively, higher and lower than most of the cited works.

Sistema de Navegação Autônomo Baseado em Visão Computacional

http://arxiv.org/pdf/1710.06518v1

Title Sistema de Navegação Autônomo Baseado em Visão Computacional Summary Autonomous robot used tool solve many kind problem environmental mapping monitoring Either adverse condition related human presence even need reduce cost certain many effort made develop robot increasingly high level autonomy must capable locomotion dynamic environment without human operator assistant system help noted thus form perception modeling environment becomes significantly relevant navigation Among main sensing method based vision possible create highlydetailed model environment since many characteristic measured texture color illumination However accurate visionbased navigation technique computationally expensive run lowcost mobile platform Therefore goal work develop lowcost robot controlled Raspberry Pi whose navigation system based vision purpose strategy used consisted identifying obstacle via optical flow pattern recognition signal possible infer relative displacement robot element environment estimation done using LucasKanade algorithm executed Raspberry Pi without harming performance Finally SVM based classifier used identify pattern signal associated obstacle movement developed system evaluated considering execution optical flow pattern dataset extracted real navigation environment end verified processing frequency system superior others Furthermore accuracy acquisition cost respectively higher lower cited work 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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['Tom Tirer', 'Raja Giryes']

1710.06647v3

Inverse problems appear in many applications such as image deblurring and inpainting. The common approach to address them is to design a specific algorithm for each problem. The Plug-and-Play (P&P) framework, which has been recently introduced, allows solving general inverse problems by leveraging the impressive capabilities of existing denoising algorithms. While this fresh strategy has found many applications, a burdensome parameter tuning is often required in order to obtain high-quality results. In this work, we propose an alternative method for solving inverse problems using denoising algorithms, which requires less parameter tuning. We provide a theoretical analysis of the method, and empirically demonstrate that it is competitive with task-specific techniques and the P&P approach for image inpainting and deblurring.

Image Restoration by Iterative Denoising and Backward Projections

http://arxiv.org/pdf/1710.06647v3

Title Image Restoration Iterative Denoising Backward Projections Summary Inverse problem appear many application image deblurring inpainting common approach address design specific algorithm problem PlugandPlay PP framework recently introduced allows solving general inverse problem leveraging impressive capability existing denoising algorithm fresh strategy found many application burdensome parameter tuning often required order obtain highquality result work propose alternative method solving inverse problem using denoising algorithm requires le parameter tuning provide theoretical analysis method empirically demonstrate competitive taskspecific technique PP approach image inpainting deblurring 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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['Johan Ekekrantz', 'Nils Bore', 'Rares Ambrus', 'John Folkesson', 'Patric Jensfelt']

1710.06929v1

In this paper we introduce a system for unsupervised object discovery and segmentation of RGBD-images. The system models the sensor noise directly from data, allowing accurate segmentation without sensor specific hand tuning of measurement noise models making use of the recently introduced Statistical Inlier Estimation (SIE) method. Through a fully probabilistic formulation, the system is able to apply probabilistic inference, enabling reliable segmentation in previously challenging scenarios. In addition, we introduce new methods for filtering out false positives, significantly improving the signal to noise ratio. We show that the system significantly outperform state-of-the-art in on a challenging real-world dataset.

Unsupervised Object Discovery and Segmentation of RGBD-images

http://arxiv.org/pdf/1710.06929v1

Title Unsupervised Object Discovery Segmentation RGBDimages Summary paper introduce system unsupervised object discovery segmentation RGBDimages system model sensor noise directly data allowing accurate segmentation without sensor specific hand tuning measurement noise model making use recently introduced Statistical Inlier Estimation SIE method fully probabilistic formulation system able apply probabilistic inference enabling reliable segmentation previously challenging scenario addition introduce new method filtering false positive significantly improving signal noise ratio show system significantly outperform stateoftheart challenging realworld dataset 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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['Octavio Arriaga', 'Matias Valdenegro-Toro', 'Paul Plöger']

1710.07557v1

In this paper we propose an implement a general convolutional neural network (CNN) building framework for designing real-time CNNs. We validate our models by creating a real-time vision system which accomplishes the tasks of face detection, gender classification and emotion classification simultaneously in one blended step using our proposed CNN architecture. After presenting the details of the training procedure setup we proceed to evaluate on standard benchmark sets. We report accuracies of 96% in the IMDB gender dataset and 66% in the FER-2013 emotion dataset. Along with this we also introduced the very recent real-time enabled guided back-propagation visualization technique. Guided back-propagation uncovers the dynamics of the weight changes and evaluates the learned features. We argue that the careful implementation of modern CNN architectures, the use of the current regularization methods and the visualization of previously hidden features are necessary in order to reduce the gap between slow performances and real-time architectures. Our system has been validated by its deployment on a Care-O-bot 3 robot used during RoboCup@Home competitions. All our code, demos and pre-trained architectures have been released under an open-source license in our public repository.

Real-time Convolutional Neural Networks for Emotion and Gender Classification

http://arxiv.org/pdf/1710.07557v1

Title Realtime Convolutional Neural Networks Emotion Gender Classification Summary paper propose implement general convolutional neural network CNN building framework designing realtime CNNs validate model creating realtime vision system accomplishes task face detection gender classification emotion classification simultaneously one blended step using proposed CNN architecture presenting detail training procedure setup proceed evaluate standard benchmark set report accuracy 96 IMDB gender dataset 66 FER2013 emotion dataset Along also introduced recent realtime enabled guided backpropagation visualization technique Guided backpropagation uncovers dynamic weight change evaluates learned feature argue careful implementation modern CNN architecture use current regularization method visualization previously hidden feature necessary order reduce gap slow performance realtime architecture system validated deployment CareObot 3 robot used RoboCupHome competition code demo pretrained architecture released opensource license public repository 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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['Mo Zhang', 'Xiang Li', 'Mengjia Xu', 'Quanzheng Li']

1710.08149v3

Reliable cell segmentation and classification from biomedical images is a crucial step for both scientific research and clinical practice. A major challenge for more robust segmentation and classification methods is the large variations in the size, shape and viewpoint of the cells, combining with the low image quality caused by noise and artifacts. To address this issue, in this work we propose a learning-based, simultaneous cell segmentation and classification method based on the deep U-Net structure with deformable convolution layers. The U-Net architecture for deep learning has been shown to offer a precise localization for image semantic segmentation. Moreover, deformable convolution layer enables the free form deformation of the feature learning process, thus makes the whole network more robust to various cell morphologies and image settings. The proposed method is tested on microscopic red blood cell images from patients with sickle cell disease. The results show that U-Net with deformable convolution achieves the highest accuracy for segmentation and classification, comparing with original U-Net structure.

Image Segmentation and Classification for Sickle Cell Disease using Deformable U-Net

http://arxiv.org/pdf/1710.08149v3

Title Image Segmentation Classification Sickle Cell Disease using Deformable UNet Summary Reliable cell segmentation classification biomedical image crucial step scientific research clinical practice major challenge robust segmentation classification method large variation size shape viewpoint cell combining low image quality caused noise artifact address issue work propose learningbased simultaneous cell segmentation classification method based deep UNet structure deformable convolution layer UNet architecture deep learning shown offer precise localization image semantic segmentation Moreover deformable convolution layer enables free form deformation feature learning process thus make whole network robust various cell morphology image setting proposed method tested microscopic red blood cell image patient sickle cell disease result show UNet deformable convolution achieves highest accuracy segmentation classification comparing original UNet structure 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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['Tomoyoshi Shimobaba', 'Yutaka Endo', 'Takashi Nishitsuji', 'Takayuki Takahashi', 'Yuki Nagahama', 'Satoki Hasegawa', 'Marie Sano', 'Ryuji Hirayama', 'Takashi Kakue', 'Atsushi Shiraki', 'Tomoyoshi Ito']

1710.08343v1

Computational ghost imaging (CGI) is a single-pixel imaging technique that exploits the correlation between known random patterns and the measured intensity of light transmitted (or reflected) by an object. Although CGI can obtain two- or three- dimensional images with a single or a few bucket detectors, the quality of the reconstructed images is reduced by noise due to the reconstruction of images from random patterns. In this study, we improve the quality of CGI images using deep learning. A deep neural network is used to automatically learn the features of noise-contaminated CGI images. After training, the network is able to predict low-noise images from new noise-contaminated CGI images.

Computational ghost imaging using deep learning

http://arxiv.org/pdf/1710.08343v1

Title Computational ghost imaging using deep learning Summary Computational ghost imaging CGI singlepixel imaging technique exploit correlation known random pattern measured intensity light transmitted reflected object Although CGI obtain two three dimensional image single bucket detector quality reconstructed image reduced noise due reconstruction image random pattern study improve quality CGI image using deep learning deep neural network used automatically learn feature noisecontaminated CGI image training network able predict lownoise image new noisecontaminated CGI image 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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['Chaitanya Mitash', 'Abdeslam Boularias', 'Kostas E. Bekris']

1710.08577v1

This work proposes a process for efficiently searching over combinations of individual object 6D pose hypotheses in cluttered scenes, especially in cases involving occlusions and objects resting on each other. The initial set of candidate object poses is generated from state-of-the-art object detection and global point cloud registration techniques. The best-scored pose per object by using these techniques may not be accurate due to overlaps and occlusions. Nevertheless, experimental indications provided in this work show that object poses with lower ranks may be closer to the real poses than ones with high ranks according to registration techniques. This motivates a global optimization process for improving these poses by taking into account scene-level physical interactions between objects. It also implies that the Cartesian product of candidate poses for interacting objects must be searched so as to identify the best scene-level hypothesis. To perform the search efficiently, the candidate poses for each object are clustered so as to reduce their number but still keep a sufficient diversity. Then, searching over the combinations of candidate object poses is performed through a Monte Carlo Tree Search (MCTS) process that uses the similarity between the observed depth image of the scene and a rendering of the scene given the hypothesized pose as a score that guides the search procedure. MCTS handles in a principled way the tradeoff between fine-tuning the most promising poses and exploring new ones, by using the Upper Confidence Bound (UCB) technique. Experimental results indicate that this process is able to quickly identify in cluttered scenes physically-consistent object poses that are significantly closer to ground truth compared to poses found by point cloud registration methods.

Improving 6D Pose Estimation of Objects in Clutter via Physics-aware Monte Carlo Tree Search

http://arxiv.org/pdf/1710.08577v1

Title Improving 6D Pose Estimation Objects Clutter via Physicsaware Monte Carlo Tree Search Summary work proposes process efficiently searching combination individual object 6D pose hypothesis cluttered scene especially case involving occlusion object resting initial set candidate object pose generated stateoftheart object detection global point cloud registration technique bestscored pose per object using technique may accurate due overlap occlusion Nevertheless experimental indication provided work show object pose lower rank may closer real pose one high rank according registration technique motivates global optimization process improving pose taking account scenelevel physical interaction object also implies Cartesian product candidate pose interacting object must searched identify best scenelevel hypothesis perform search efficiently candidate pose object clustered reduce number still keep sufficient diversity searching combination candidate object pose performed Monte Carlo Tree Search MCTS process us similarity observed depth image scene rendering scene given hypothesized pose score guide search procedure MCTS handle principled way tradeoff finetuning promising pose exploring new one using Upper Confidence Bound UCB technique Experimental result indicate process able quickly identify cluttered scene physicallyconsistent object pose significantly closer ground truth compared pose found point cloud registration 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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['Gunjan Sehgal', 'Bindu Gupta', 'Kaushal Paneri', 'Karamjit Singh', 'Geetika Sharma', 'Gautam Shroff']

1710.09077v1

As the world population increases and arable land decreases, it becomes vital to improve the productivity of the agricultural land available. Given the weather and soil properties, farmers need to take critical decisions such as which seed variety to plant and in what proportion, in order to maximize productivity. These decisions are irreversible and any unusual behavior of external factors, such as weather, can have catastrophic impact on the productivity of crop. A variety which is highly desirable to a farmer might be unavailable or in short supply, therefore, it is very critical to evaluate which variety or varieties are more likely to be chosen by farmers from a growing region in order to meet demand. In this paper, we present our visual analytics tool, ViSeed, showcased on the data given in Syngenta 2016 crop data challenge 1 . This tool helps to predict optimal soybean seed variety or mix of varieties in appropriate proportions which is more likely to be chosen by farmers from a growing region. It also allows to analyse solutions generated from our approach and helps in the decision making process by providing insightful visualizations

Crop Planning using Stochastic Visual Optimization

http://arxiv.org/pdf/1710.09077v1

Title Crop Planning using Stochastic Visual Optimization Summary world population increase arable land decrease becomes vital improve productivity agricultural land available Given weather soil property farmer need take critical decision seed variety plant proportion order maximize productivity decision irreversible unusual behavior external factor weather catastrophic impact productivity crop variety highly desirable farmer might unavailable short supply therefore critical evaluate variety variety likely chosen farmer growing region order meet demand paper present visual analytics tool ViSeed showcased data given Syngenta 2016 crop data challenge 1 tool help predict optimal soybean seed variety mix variety appropriate proportion likely chosen farmer growing region also allows analyse solution generated approach help decision making process providing insightful visualization 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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['Srivatsa Prativadibhayankaram', 'Huynh Van Luong', 'Thanh-Ha Le', 'Andre Kaup']

1710.09160v1

In the context of online Robust Principle Component Analysis (RPCA) for the video foreground-background separation, we propose a compressive online RPCA with optical flow that separates recursively a sequence of frames into sparse (foreground) and low-rank (background) components. Our method considers a small set of measurements taken per data vector (frame), which is different from conventional batch RPCA, processing all the data directly. The proposed method also incorporates multiple prior information, namely previous foreground and background frames, to improve the separation and then updates the prior information for the next frame. Moreover, the foreground prior frames are improved by estimating motions between the previous foreground frames using optical flow and compensating the motions to achieve higher quality foreground prior. The proposed method is applied to online video foreground and background separation from compressive measurements. The visual and quantitative results show that our method outperforms the existing methods.

Compressive Online Robust Principal Component Analysis with Optical Flow for Video Foreground-Background Separation

http://arxiv.org/pdf/1710.09160v1

Title Compressive Online Robust Principal Component Analysis Optical Flow Video ForegroundBackground Separation Summary context online Robust Principle Component Analysis RPCA video foregroundbackground separation propose compressive online RPCA optical flow separate recursively sequence frame sparse foreground lowrank background component method considers small set measurement taken per data vector frame different conventional batch RPCA processing data directly proposed method also incorporates multiple prior information namely previous foreground background frame improve separation update prior information next frame Moreover foreground prior frame improved estimating motion previous foreground frame using optical flow compensating motion achieve higher quality foreground prior proposed method applied online video foreground background separation compressive measurement visual quantitative result show method outperforms existing 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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['Veeru Talreja', 'Terry Ferrett', 'Matthew C. Valenti', 'Arun Ross']

1710.09183v1

Biometric recognition, or simply biometrics, is the use of biological attributes such as face, fingerprints or iris in order to recognize an individual in an automated manner. A key application of biometrics is authentication; i.e., using said biological attributes to provide access by verifying the claimed identity of an individual. This paper presents a framework for Biometrics-as-a-Service (BaaS) that performs biometric matching operations in the cloud, while relying on simple and ubiquitous consumer devices such as smartphones. Further, the framework promotes innovation by providing interfaces for a plurality of software developers to upload their matching algorithms to the cloud. When a biometric authentication request is submitted, the system uses a criteria to automatically select an appropriate matching algorithm. Every time a particular algorithm is selected, the corresponding developer is rendered a micropayment. This creates an innovative and competitive ecosystem that benefits both software developers and the consumers. As a case study, we have implemented the following: (a) an ocular recognition system using a mobile web interface providing user access to a biometric authentication service, and (b) a Linux-based virtual machine environment used by software developers for algorithm development and submission.

Biometrics-as-a-Service: A Framework to Promote Innovative Biometric Recognition in the Cloud

http://arxiv.org/pdf/1710.09183v1

Title BiometricsasaService Framework Promote Innovative Biometric Recognition Cloud Summary Biometric recognition simply biometrics use biological attribute face fingerprint iris order recognize individual automated manner key application biometrics authentication ie using said biological attribute provide access verifying claimed identity individual paper present framework BiometricsasaService BaaS performs biometric matching operation cloud relying simple ubiquitous consumer device smartphones framework promotes innovation providing interface plurality software developer upload matching algorithm cloud biometric authentication request submitted system us criterion automatically select appropriate matching algorithm Every time particular algorithm selected corresponding developer rendered micropayment creates innovative competitive ecosystem benefit software developer consumer case study implemented following ocular recognition system using mobile web interface providing user access biometric authentication service b Linuxbased virtual machine environment used software developer algorithm development submission 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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['Matthew Berger', 'Jixian Li', 'Joshua A. Levine']

1710.09545v1

We present a technique to synthesize and analyze volume-rendered images using generative models. We use the Generative Adversarial Network (GAN) framework to compute a model from a large collection of volume renderings, conditioned on (1) viewpoint and (2) transfer functions for opacity and color. Our approach facilitates tasks for volume analysis that are challenging to achieve using existing rendering techniques such as ray casting or texture-based methods. We show how to guide the user in transfer function editing by quantifying expected change in the output image. Additionally, the generative model transforms transfer functions into a view-invariant latent space specifically designed to synthesize volume-rendered images. We use this space directly for rendering, enabling the user to explore the space of volume-rendered images. As our model is independent of the choice of volume rendering process, we show how to analyze volume-rendered images produced by direct and global illumination lighting, for a variety of volume datasets.

A Generative Model for Volume Rendering

http://arxiv.org/pdf/1710.09545v1

Title Generative Model Volume Rendering Summary present technique synthesize analyze volumerendered image using generative model use Generative Adversarial Network GAN framework compute model large collection volume rendering conditioned 1 viewpoint 2 transfer function opacity color approach facilitates task volume analysis challenging achieve using existing rendering technique ray casting texturebased method show guide user transfer function editing quantifying expected change output image Additionally generative model transforms transfer function viewinvariant latent space specifically designed synthesize volumerendered image use space directly rendering enabling user explore space volumerendered image model independent choice volume rendering process show analyze volumerendered image produced direct global illumination lighting variety volume 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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['Rong Kang', 'Chen Wang', 'Peng Wang', 'Yuting Ding', 'Jianmin Wang']

1710.10088v3

Pattern matching of streaming time series with lower latency under limited computing resource comes to a critical problem, especially as the growth of Industry 4.0 and Industry Internet of Things. However, against traditional single pattern matching problem, a pattern may contain multiple segments representing different statistical properties or physical meanings for more precise and expressive matching in real world. Hence, we formulate a new problem, called "fine-grained pattern matching", which allows users to specify varied granularities of matching deviation to different segments of a given pattern, and fuzzy regions for adaptive breakpoints determination between consecutive segments. In this paper, we propose a novel two-phase approach. In the pruning phase, we introduce Equal-Length Block (ELB) representation together with Block-Skipping Pruning (BSP) policy, which guarantees low cost feature calculation, effective pruning and no false dismissals. In the post-processing phase, a delta-function is proposed to enable us to conduct exact matching in linear complexity. Extensive experiments are conducted to evaluate on synthetic and real-world datasets, which illustrates that our algorithm outperforms the brute-force method and MSM, a multi-step filter mechanism over the multi-scaled representation.

Fine-grained Pattern Matching Over Streaming Time Series

http://arxiv.org/pdf/1710.10088v3

Title Finegrained Pattern Matching Streaming Time Series Summary Pattern matching streaming time series lower latency limited computing resource come critical problem especially growth Industry 40 Industry Internet Things However traditional single pattern matching problem pattern may contain multiple segment representing different statistical property physical meaning precise expressive matching real world Hence formulate new problem called finegrained pattern matching allows user specify varied granularity matching deviation different segment given pattern fuzzy region adaptive breakpoints determination consecutive segment paper propose novel twophase approach pruning phase introduce EqualLength Block ELB representation together BlockSkipping Pruning BSP policy guarantee low cost feature calculation effective pruning false dismissal postprocessing phase deltafunction proposed enable u conduct exact matching linear complexity Extensive experiment conducted evaluate synthetic realworld datasets illustrates algorithm outperforms bruteforce method MSM multistep filter mechanism multiscaled representation 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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