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['Francisco Gomez-Donoso', 'Sergio Orts-Escolano', 'Miguel Cazorla']

1707.03742v3

Accurate hand pose estimation at joint level has several uses on human-robot interaction, user interfacing and virtual reality applications. Yet, it currently is not a solved problem. The novel deep learning techniques could make a great improvement on this matter but they need a huge amount of annotated data. The hand pose datasets released so far present some issues that make them impossible to use on deep learning methods such as the few number of samples, high-level abstraction annotations or samples consisting in depth maps. In this work, we introduce a multiview hand pose dataset in which we provide color images of hands and different kind of annotations for each, i.e the bounding box and the 2D and 3D location on the joints in the hand. Besides, we introduce a simple yet accurate deep learning architecture for real-time robust 2D hand pose estimation.

Large-scale Multiview 3D Hand Pose Dataset

http://arxiv.org/pdf/1707.03742v3

Title Largescale Multiview 3D Hand Pose Dataset Summary Accurate hand pose estimation joint level several us humanrobot interaction user interfacing virtual reality application Yet currently solved problem novel deep learning technique could make great improvement matter need huge amount annotated data hand pose datasets released far present issue make impossible use deep learning method number sample highlevel abstraction annotation sample consisting depth map work introduce multiview hand pose dataset provide color image hand different kind annotation ie bounding box 2D 3D location joint hand Besides introduce simple yet accurate deep learning architecture realtime robust 2D hand pose estimation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['George Terzakis', 'Riccardo Polvara', 'Sanjay Sharma', 'Phil Culverhouse', 'Robert Sutton']

1707.04444v2

We tackle the problem of localizing an autonomous sea-surface vehicle in river estuarine areas using monocular camera and angular velocity input from an inertial sensor. Our method is challenged by two prominent drawbacks associated with the environment, which are typically not present in standard visual simultaneous localization and mapping (SLAM) applications on land (or air): a) Scene depth varies significantly (from a few meters to several kilometers) and, b) In conjunction to the latter, there exists no ground plane to provide features with enough disparity based on which to reliably detect motion. To that end, we use the IMU orientation feedback in order to re-cast the problem of visual localization without the mapping component, although the map can be implicitly obtained from the camera pose estimates. We find that our method produces reliable odometry estimates for trajectories several hundred meters long in the water. To compare the visual odometry estimates with GPS based ground truth, we interpolate the trajectory with splines on a common parameter and obtain position error in meters recovering an optimal affine transformation between the two splines.

Monocular Visual Odometry for an Unmanned Sea-Surface Vehicle

http://arxiv.org/pdf/1707.04444v2

Title Monocular Visual Odometry Unmanned SeaSurface Vehicle Summary tackle problem localizing autonomous seasurface vehicle river estuarine area using monocular camera angular velocity input inertial sensor method challenged two prominent drawback associated environment typically present standard visual simultaneous localization mapping SLAM application land air Scene depth varies significantly meter several kilometer b conjunction latter exists ground plane provide feature enough disparity based reliably detect motion end use IMU orientation feedback order recast problem visual localization without mapping component although map implicitly obtained camera pose estimate find method produce reliable odometry estimate trajectory several hundred meter long water compare visual odometry estimate GPS based ground truth interpolate trajectory spline common parameter obtain position error meter recovering optimal affine transformation two spline 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 Rubin', 'Rui Abreu', 'Anurag Ganguli', 'Saigopal Nelaturi', 'Ion Matei', 'Kumar Sricharan']

1707.04642v2

The work presented here applies deep learning to the task of automated cardiac auscultation, i.e. recognizing abnormalities in heart sounds. We describe an automated heart sound classification algorithm that combines the use of time-frequency heat map representations with a deep convolutional neural network (CNN). Given the cost-sensitive nature of misclassification, our CNN architecture is trained using a modified loss function that directly optimizes the trade-off between sensitivity and specificity. We evaluated our algorithm at the 2016 PhysioNet Computing in Cardiology challenge where the objective was to accurately classify normal and abnormal heart sounds from single, short, potentially noisy recordings. Our entry to the challenge achieved a final specificity of 0.95, sensitivity of 0.73 and overall score of 0.84. We achieved the greatest specificity score out of all challenge entries and, using just a single CNN, our algorithm differed in overall score by only 0.02 compared to the top place finisher, which used an ensemble approach.

Recognizing Abnormal Heart Sounds Using Deep Learning

http://arxiv.org/pdf/1707.04642v2

Title Recognizing Abnormal Heart Sounds Using Deep Learning Summary work presented applies deep learning task automated cardiac auscultation ie recognizing abnormality heart sound describe automated heart sound classification algorithm combine use timefrequency heat map representation deep convolutional neural network CNN Given costsensitive nature misclassification CNN architecture trained using modified loss function directly optimizes tradeoff sensitivity specificity evaluated algorithm 2016 PhysioNet Computing Cardiology challenge objective accurately classify normal abnormal heart sound single short potentially noisy recording entry challenge achieved final specificity 095 sensitivity 073 overall score 084 achieved greatest specificity score challenge entry using single CNN algorithm differed overall score 002 compared top place finisher used ensemble 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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['Pat Marion', 'Peter R. Florence', 'Lucas Manuelli', 'Russ Tedrake']

1707.04796v3

Deep neural network (DNN) architectures have been shown to outperform traditional pipelines for object segmentation and pose estimation using RGBD data, but the performance of these DNN pipelines is directly tied to how representative the training data is of the true data. Hence a key requirement for employing these methods in practice is to have a large set of labeled data for your specific robotic manipulation task, a requirement that is not generally satisfied by existing datasets. In this paper we develop a pipeline to rapidly generate high quality RGBD data with pixelwise labels and object poses. We use an RGBD camera to collect video of a scene from multiple viewpoints and leverage existing reconstruction techniques to produce a 3D dense reconstruction. We label the 3D reconstruction using a human assisted ICP-fitting of object meshes. By reprojecting the results of labeling the 3D scene we can produce labels for each RGBD image of the scene. This pipeline enabled us to collect over 1,000,000 labeled object instances in just a few days. We use this dataset to answer questions related to how much training data is required, and of what quality the data must be, to achieve high performance from a DNN architecture.

LabelFusion: A Pipeline for Generating Ground Truth Labels for Real RGBD Data of Cluttered Scenes

http://arxiv.org/pdf/1707.04796v3

Title LabelFusion Pipeline Generating Ground Truth Labels Real RGBD Data Cluttered Scenes Summary Deep neural network DNN architecture shown outperform traditional pipeline object segmentation pose estimation using RGBD data performance DNN pipeline directly tied representative training data true data Hence key requirement employing method practice large set labeled data specific robotic manipulation task requirement generally satisfied existing datasets paper develop pipeline rapidly generate high quality RGBD data pixelwise label object pose use RGBD camera collect video scene multiple viewpoint leverage existing reconstruction technique produce 3D dense reconstruction label 3D reconstruction using human assisted ICPfitting object mesh reprojecting result labeling 3D scene produce label RGBD image scene pipeline enabled u collect 1000000 labeled object instance day use dataset answer question related much training data required quality data must achieve high performance DNN architecture 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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['Ioannis Rigas', 'Hayes Raffle', 'Oleg V. Komogortsev']

1707.05411v2

This paper introduces and evaluates a hybrid technique that fuses efficiently the eye-tracking principles of photosensor oculography (PSOG) and video oculography (VOG). The main concept of this novel approach is to use a few fast and power-economic photosensors as the core mechanism for performing high speed eye-tracking, whereas in parallel, use a video sensor operating at low sampling-rate (snapshot mode) to perform dead-reckoning error correction when sensor movements occur. In order to evaluate the proposed method, we simulate the functional components of the technique and present our results in experimental scenarios involving various combinations of horizontal and vertical eye and sensor movements. Our evaluation shows that the developed technique can be used to provide robustness to sensor shifts that otherwise could induce error larger than 5 deg. Our analysis suggests that the technique can potentially enable high speed eye-tracking at low power profiles, making it suitable to be used in emerging head-mounted devices, e.g. AR/VR headsets.

Hybrid PS-V Technique: A Novel Sensor Fusion Approach for Fast Mobile Eye-Tracking with Sensor-Shift Aware Correction

http://arxiv.org/pdf/1707.05411v2

Title Hybrid PSV Technique Novel Sensor Fusion Approach Fast Mobile EyeTracking SensorShift Aware Correction Summary paper introduces evaluates hybrid technique fuse efficiently eyetracking principle photosensor oculography PSOG video oculography VOG main concept novel approach use fast powereconomic photosensors core mechanism performing high speed eyetracking whereas parallel use video sensor operating low samplingrate snapshot mode perform deadreckoning error correction sensor movement occur order evaluate proposed method simulate functional component technique present result experimental scenario involving various combination horizontal vertical eye sensor movement evaluation show developed technique used provide robustness sensor shift otherwise could induce error larger 5 deg analysis suggests technique potentially enable high speed eyetracking low power profile making suitable used emerging headmounted device eg ARVR headset 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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['Ioannis Rigas', 'Hayes Raffle', 'Oleg V. Komogortsev']

1707.05413v2

This paper presents a renewed overview of photosensor oculography (PSOG), an eye-tracking technique based on the principle of using simple photosensors to measure the amount of reflected (usually infrared) light when the eye rotates. Photosensor oculography can provide measurements with high precision, low latency and reduced power consumption, and thus it appears as an attractive option for performing eye-tracking in the emerging head-mounted interaction devices, e.g. augmented and virtual reality (AR/VR) headsets. In our current work we employ an adjustable simulation framework as a common basis for performing an exploratory study of the eye-tracking behavior of different photosensor oculography designs. With the performed experiments we explore the effects from the variation of some basic parameters of the designs on the resulting accuracy and cross-talk, which are crucial characteristics for the seamless operation of human-computer interaction applications based on eye-tracking. Our experimental results reveal the design trade-offs that need to be adopted to tackle the competing conditions that lead to optimum performance of different eye-tracking characteristics. We also present the transformations that arise in the eye-tracking output when sensor shifts occur, and assess the resulting degradation in accuracy for different combinations of eye movements and sensor shifts.

Photosensor Oculography: Survey and Parametric Analysis of Designs using Model-Based Simulation

http://arxiv.org/pdf/1707.05413v2

Title Photosensor Oculography Survey Parametric Analysis Designs using ModelBased Simulation Summary paper present renewed overview photosensor oculography PSOG eyetracking technique based principle using simple photosensors measure amount reflected usually infrared light eye rotates Photosensor oculography provide measurement high precision low latency reduced power consumption thus appears attractive option performing eyetracking emerging headmounted interaction device eg augmented virtual reality ARVR headset current work employ adjustable simulation framework common basis performing exploratory study eyetracking behavior different photosensor oculography design performed experiment explore effect variation basic parameter design resulting accuracy crosstalk crucial characteristic seamless operation humancomputer interaction application based eyetracking experimental result reveal design tradeoff need adopted tackle competing condition lead optimum performance different eyetracking characteristic also present transformation arise eyetracking output sensor shift occur ass resulting degradation accuracy different combination eye movement sensor shift 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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['Qianggong Zhang', 'Tat-Jun Chin']

1707.05466v1

Multiple-view triangulation by $\ell_{\infty}$ minimisation has become established in computer vision. State-of-the-art $\ell_{\infty}$ triangulation algorithms exploit the quasiconvexity of the cost function to derive iterative update rules that deliver the global minimum. Such algorithms, however, can be computationally costly for large problem instances that contain many image measurements, e.g., from web-based photo sharing sites or long-term video recordings. In this paper, we prove that $\ell_{\infty}$ triangulation admits a coreset approximation scheme, which seeks small representative subsets of the input data called coresets. A coreset possesses the special property that the error of the $\ell_{\infty}$ solution on the coreset is within known bounds from the global minimum. We establish the necessary mathematical underpinnings of the coreset algorithm, specifically, by enacting the stopping criterion of the algorithm and proving that the resulting coreset gives the desired approximation accuracy. On large-scale triangulation problems, our method provides theoretically sound approximate solutions. Iterated until convergence, our coreset algorithm is also guaranteed to reach the true optimum. On practical datasets, we show that our technique can in fact attain the global minimiser much faster than current methods

Coresets for Triangulation

http://arxiv.org/pdf/1707.05466v1

Title Coresets Triangulation Summary Multipleview triangulation ellinfty minimisation become established computer vision Stateoftheart ellinfty triangulation algorithm exploit quasiconvexity cost function derive iterative update rule deliver global minimum algorithm however computationally costly large problem instance contain many image measurement eg webbased photo sharing site longterm video recording paper prove ellinfty triangulation admits coreset approximation scheme seek small representative subset input data called coresets coreset posse special property error ellinfty solution coreset within known bound global minimum establish necessary mathematical underpinnings coreset algorithm specifically enacting stopping criterion algorithm proving resulting coreset give desired approximation accuracy largescale triangulation problem method provides theoretically sound approximate solution Iterated convergence coreset algorithm also guaranteed reach true optimum practical datasets show technique fact attain global minimiser much faster current 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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['Thomas Shortell', 'Ali Shokoufandeh']

1707.05905v1

Cloud computing is an important part of today's world because offloading computations is a method to reduce costs. In this paper, we investigate computing the Speeded Up Robust Features (SURF) using Fully Homomorphic Encryption (FHE). Performing SURF in FHE enables a method to offload the computations while maintaining security and privacy of the original data. In support of this research, we developed a framework to compute SURF via a rational number based compatible with FHE. Although floating point (R) to rational numbers (Q) conversion introduces error, our research provides tight bounds on the magnitude of error in terms of parameters of FHE. We empirically verified the proposed method against a set of images at different sizes and showed that our framework accurately computes most of the SURF keypoints in FHE.

Secure SURF with Fully Homomorphic Encryption

http://arxiv.org/pdf/1707.05905v1

Title Secure SURF Fully Homomorphic Encryption Summary Cloud computing important part today world offloading computation method reduce cost paper investigate computing Speeded Robust Features SURF using Fully Homomorphic Encryption FHE Performing SURF FHE enables method offload computation maintaining security privacy original data support research developed framework compute SURF via rational number based compatible FHE Although floating point R rational number Q conversion introduces error research provides tight bound magnitude error term parameter FHE empirically verified proposed method set image different size showed framework accurately computes SURF keypoints FHE 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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['Anton V. Eremeev', 'Alexander V. Kelmanov', 'Artem V. Pyatkin', 'Igor A. Ziegler']

1707.06068v1

In this paper, we consider the problem of finding a maximum cardinality subset of vectors, given a constraint on the normalized squared length of vectors sum. This problem is closely related to Problem 1 from (Eremeev, Kel'manov, Pyatkin, 2016). The main difference consists in swapping the constraint with the optimization criterion. We prove that the problem is NP-hard even in terms of finding a feasible solution. An exact algorithm for solving this problem is proposed. The algorithm has a pseudo-polynomial time complexity in the special case of the problem, where the dimension of the space is bounded from above by a constant and the input data are integer. A computational experiment is carried out, where the proposed algorithm is compared to COINBONMIN solver, applied to a mixed integer quadratic programming formulation of the problem. The results of the experiment indicate superiority of the proposed algorithm when the dimension of Euclidean space is low, while the COINBONMIN has an advantage for larger dimensions.

On Finding Maximum Cardinality Subset of Vectors with a Constraint on Normalized Squared Length of Vectors Sum

http://arxiv.org/pdf/1707.06068v1

Title Finding Maximum Cardinality Subset Vectors Constraint Normalized Squared Length Vectors Sum Summary paper consider problem finding maximum cardinality subset vector given constraint normalized squared length vector sum problem closely related Problem 1 Eremeev Kelmanov Pyatkin 2016 main difference consists swapping constraint optimization criterion prove problem NPhard even term finding feasible solution exact algorithm solving problem proposed algorithm pseudopolynomial time complexity special case problem dimension space bounded constant input data integer computational experiment carried proposed algorithm compared COINBONMIN solver applied mixed integer quadratic programming formulation problem result experiment indicate superiority proposed algorithm dimension Euclidean space low COINBONMIN advantage larger dimension 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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['Matheus Gadelha', 'Subhransu Maji', 'Rui Wang']

1707.06267v1

We propose a method to generate 3D shapes using point clouds. Given a point-cloud representation of a 3D shape, our method builds a kd-tree to spatially partition the points. This orders them consistently across all shapes, resulting in reasonably good correspondences across all shapes. We then use PCA analysis to derive a linear shape basis across the spatially partitioned points, and optimize the point ordering by iteratively minimizing the PCA reconstruction error. Even with the spatial sorting, the point clouds are inherently noisy and the resulting distribution over the shape coefficients can be highly multi-modal. We propose to use the expressive power of neural networks to learn a distribution over the shape coefficients in a generative-adversarial framework. Compared to 3D shape generative models trained on voxel-representations, our point-based method is considerably more light-weight and scalable, with little loss of quality. It also outperforms simpler linear factor models such as Probabilistic PCA, both qualitatively and quantitatively, on a number of categories from the ShapeNet dataset. Furthermore, our method can easily incorporate other point attributes such as normal and color information, an additional advantage over voxel-based representations.

Shape Generation using Spatially Partitioned Point Clouds

http://arxiv.org/pdf/1707.06267v1

Title Shape Generation using Spatially Partitioned Point Clouds Summary propose method generate 3D shape using point cloud Given pointcloud representation 3D shape method build kdtree spatially partition point order consistently across shape resulting reasonably good correspondence across shape use PCA analysis derive linear shape basis across spatially partitioned point optimize point ordering iteratively minimizing PCA reconstruction error Even spatial sorting point cloud inherently noisy resulting distribution shape coefficient highly multimodal propose use expressive power neural network learn distribution shape coefficient generativeadversarial framework Compared 3D shape generative model trained voxelrepresentations pointbased method considerably lightweight scalable little loss quality also outperforms simpler linear factor model Probabilistic PCA qualitatively quantitatively number category ShapeNet dataset Furthermore method easily incorporate point attribute normal color information additional advantage voxelbased 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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['Aleksander Klibisz', 'Derek Rose', 'Matthew Eicholtz', 'Jay Blundon', 'Stanislav Zakharenko']

1707.06314v1

Calcium imaging is a technique for observing neuron activity as a series of images showing indicator fluorescence over time. Manually segmenting neurons is time-consuming, leading to research on automated calcium imaging segmentation (ACIS). We evaluated several deep learning models for ACIS on the Neurofinder competition datasets and report our best model: U-Net2DS, a fully convolutional network that operates on 2D mean summary images. U-Net2DS requires minimal domain-specific pre/post-processing and parameter adjustment, and predictions are made on full $512\times512$ images at $\approx$9K images per minute. It ranks third in the Neurofinder competition ($F_1=0.569$) and is the best model to exclusively use deep learning. We also demonstrate useful segmentations on data from outside the competition. The model's simplicity, speed, and quality results make it a practical choice for ACIS and a strong baseline for more complex models in the future.

Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks

http://arxiv.org/pdf/1707.06314v1

Title Fast Simple Calcium Imaging Segmentation Fully Convolutional Networks Summary Calcium imaging technique observing neuron activity series image showing indicator fluorescence time Manually segmenting neuron timeconsuming leading research automated calcium imaging segmentation ACIS evaluated several deep learning model ACIS Neurofinder competition datasets report best model UNet2DS fully convolutional network operates 2D mean summary image UNet2DS requires minimal domainspecific prepostprocessing parameter adjustment prediction made full 512times512 image approx9K image per minute rank third Neurofinder competition F10569 best model exclusively use deep learning also demonstrate useful segmentation data outside competition model simplicity speed quality result make practical choice ACIS strong baseline complex model future 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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['Yi Zhu', 'Shawn Newsam']

1707.06316v1

Classical approaches for estimating optical flow have achieved rapid progress in the last decade. However, most of them are too slow to be applied in real-time video analysis. Due to the great success of deep learning, recent work has focused on using CNNs to solve such dense prediction problems. In this paper, we investigate a new deep architecture, Densely Connected Convolutional Networks (DenseNet), to learn optical flow. This specific architecture is ideal for the problem at hand as it provides shortcut connections throughout the network, which leads to implicit deep supervision. We extend current DenseNet to a fully convolutional network to learn motion estimation in an unsupervised manner. Evaluation results on three standard benchmarks demonstrate that DenseNet is a better fit than other widely adopted CNN architectures for optical flow estimation.

DenseNet for Dense Flow

http://arxiv.org/pdf/1707.06316v1

Title DenseNet Dense Flow Summary Classical approach estimating optical flow achieved rapid progress last decade However slow applied realtime video analysis Due great success deep learning recent work focused using CNNs solve dense prediction problem paper investigate new deep architecture Densely Connected Convolutional Networks DenseNet learn optical flow specific architecture ideal problem hand provides shortcut connection throughout network lead implicit deep supervision extend current DenseNet fully convolutional network learn motion estimation unsupervised manner Evaluation result three standard benchmark demonstrate DenseNet better fit widely adopted CNN architecture optical flow estimation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Zhaoliang Lun', 'Matheus Gadelha', 'Evangelos Kalogerakis', 'Subhransu Maji', 'Rui Wang']

1707.06375v3

We propose a method for reconstructing 3D shapes from 2D sketches in the form of line drawings. Our method takes as input a single sketch, or multiple sketches, and outputs a dense point cloud representing a 3D reconstruction of the input sketch(es). The point cloud is then converted into a polygon mesh. At the heart of our method lies a deep, encoder-decoder network. The encoder converts the sketch into a compact representation encoding shape information. The decoder converts this representation into depth and normal maps capturing the underlying surface from several output viewpoints. The multi-view maps are then consolidated into a 3D point cloud by solving an optimization problem that fuses depth and normals across all viewpoints. Based on our experiments, compared to other methods, such as volumetric networks, our architecture offers several advantages, including more faithful reconstruction, higher output surface resolution, better preservation of topology and shape structure.

3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks

http://arxiv.org/pdf/1707.06375v3

Title 3D Shape Reconstruction Sketches via Multiview Convolutional Networks Summary propose method reconstructing 3D shape 2D sketch form line drawing method take input single sketch multiple sketch output dense point cloud representing 3D reconstruction input sketch point cloud converted polygon mesh heart method lie deep encoderdecoder network encoder convert sketch compact representation encoding shape information decoder convert representation depth normal map capturing underlying surface several output viewpoint multiview map consolidated 3D point cloud solving optimization problem fuse depth normal across viewpoint Based experiment compared method volumetric network architecture offer several advantage including faithful reconstruction higher output surface resolution better preservation topology shape 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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['Dominik Rueß', 'Konstantinos Amplianitis', 'Niklas Deckers', 'Michele Adduci', 'Kristian Manthey', 'Ralf Reulke']

1707.06557v1

Video surveillance always had a negative connotation, among others because of the loss of privacy and because it may not automatically increase public safety. If it was able to detect atypical (i.e. dangerous) situations in real time, autonomously and anonymously, this could change. A prerequisite for this is a reliable automatic detection of possibly dangerous situations from video data. This is done classically by object extraction and tracking. From the derived trajectories, we then want to determine dangerous situations by detecting atypical trajectories. However, due to ethical considerations it is better to develop such a system on data without people being threatened or even harmed, plus with having them know that there is such a tracking system installed. Another important point is that these situations do not occur very often in real, public CCTV areas and may be captured properly even less. In the artistic project leave a trace the tracked objects, people in an atrium of a institutional building, become actor and thus part of the installation. Visualisation in real-time allows interaction by these actors, which in turn creates many atypical interaction situations on which we can develop our situation detection. The data set has evolved over three years and hence, is huge. In this article we describe the tracking system and several approaches for the detection of atypical trajectories.

leave a trace - A People Tracking System Meets Anomaly Detection

http://arxiv.org/pdf/1707.06557v1

Title leave trace People Tracking System Meets Anomaly Detection Summary Video surveillance always negative connotation among others loss privacy may automatically increase public safety able detect atypical ie dangerous situation real time autonomously anonymously could change prerequisite reliable automatic detection possibly dangerous situation video data done classically object extraction tracking derived trajectory want determine dangerous situation detecting atypical trajectory However due ethical consideration better develop system data without people threatened even harmed plus know tracking system installed Another important point situation occur often real public CCTV area may captured properly even le artistic project leave trace tracked object people atrium institutional building become actor thus part installation Visualisation realtime allows interaction actor turn creates many atypical interaction situation develop situation detection data set evolved three year hence huge article describe tracking system several approach detection atypical 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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['Brendt Wohlberg', 'Paul Rodriguez']

1707.06718v1

Two different approaches have recently been proposed for boundary handling in convolutional sparse representations, avoiding potential boundary artifacts arising from the circular boundary conditions implied by the use of frequency domain solution methods by introducing a spatial mask into the convolutional sparse coding problem. In the present paper we show that, under certain circumstances, these methods fail in their design goal of avoiding boundary artifacts. The reasons for this failure are discussed, a solution is proposed, and the practical implications are illustrated in an image deblurring problem.

Convolutional Sparse Coding: Boundary Handling Revisited

http://arxiv.org/pdf/1707.06718v1

Title Convolutional Sparse Coding Boundary Handling Revisited Summary Two different approach recently proposed boundary handling convolutional sparse representation avoiding potential boundary artifact arising circular boundary condition implied use frequency domain solution method introducing spatial mask convolutional sparse coding problem present paper show certain circumstance method fail design goal avoiding boundary artifact reason failure discussed solution proposed practical implication illustrated image deblurring problem Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Michele Merler', 'Dhiraj Joshi', 'Quoc-Bao Nguyen', 'Stephen Hammer', 'John Kent', 'John R. Smith', 'Rogerio S. Feris']

1707.07075v1

The production of sports highlight packages summarizing a game's most exciting moments is an essential task for broadcast media. Yet, it requires labor-intensive video editing. We propose a novel approach for auto-curating sports highlights, and use it to create a real-world system for the editorial aid of golf highlight reels. Our method fuses information from the players' reactions (action recognition such as high-fives and fist pumps), spectators (crowd cheering), and commentator (tone of the voice and word analysis) to determine the most interesting moments of a game. We accurately identify the start and end frames of key shot highlights with additional metadata, such as the player's name and the hole number, allowing personalized content summarization and retrieval. In addition, we introduce new techniques for learning our classifiers with reduced manual training data annotation by exploiting the correlation of different modalities. Our work has been demonstrated at a major golf tournament, successfully extracting highlights from live video streams over four consecutive days.

Automatic Curation of Golf Highlights using Multimodal Excitement Features

http://arxiv.org/pdf/1707.07075v1

Title Automatic Curation Golf Highlights using Multimodal Excitement Features Summary production sport highlight package summarizing game exciting moment essential task broadcast medium Yet requires laborintensive video editing propose novel approach autocurating sport highlight use create realworld system editorial aid golf highlight reel method fuse information player reaction action recognition highfives fist pump spectator crowd cheering commentator tone voice word analysis determine interesting moment game accurately identify start end frame key shot highlight additional metadata player name hole number allowing personalized content summarization retrieval addition introduce new technique learning classifier reduced manual training data annotation exploiting correlation different modality work demonstrated major golf tournament successfully extracting highlight live video stream four consecutive day 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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['Kun Li', 'Joel W. Burdick']

1707.07139v2

This paper develops a method to use RGB-D cameras to track the motions of a human spinal cord injury patient undergoing spinal stimulation and physical rehabilitation. Because clinicians must remain close to the patient during training sessions, the patient is usually under permanent and transient occlusions due to the training equipment and the movements of the attending clinicians. These occlusions can significantly degrade the accuracy of existing human tracking methods. To improve the data association problem in these circumstances, we present a new global feature based on the geodesic distances of surface mesh points to a set of anchor points. Transient occlusions are handled via a multi-hypothesis tracking framework. To evaluate the method, we simulated different occlusion sizes on a data set captured from a human in varying movement patterns, and compared the proposed feature with other tracking methods. The results show that the proposed method achieves robustness to both surface deformations and transient occlusions.

Clinical Patient Tracking in the Presence of Transient and Permanent Occlusions via Geodesic Feature

http://arxiv.org/pdf/1707.07139v2

Title Clinical Patient Tracking Presence Transient Permanent Occlusions via Geodesic Feature Summary paper develops method use RGBD camera track motion human spinal cord injury patient undergoing spinal stimulation physical rehabilitation clinician must remain close patient training session patient usually permanent transient occlusion due training equipment movement attending clinician occlusion significantly degrade accuracy existing human tracking method improve data association problem circumstance present new global feature based geodesic distance surface mesh point set anchor point Transient occlusion handled via multihypothesis tracking framework evaluate method simulated different occlusion size data set captured human varying movement pattern compared proposed feature tracking method result show proposed method achieves robustness surface deformation transient occlusion 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', 'Gerardo Aragon-Camarasa', 'Simon Rogers', 'Rustam Stolkin', 'J. Paul Siebert']

1707.07157v1

This paper proposes a single-shot approach for recognising clothing categories from 2.5D features. We propose two visual features, BSP (B-Spline Patch) and TSD (Topology Spatial Distances) for this task. The local BSP features are encoded by LLC (Locality-constrained Linear Coding) and fused with three different global features. Our visual feature is robust to deformable shapes and our approach is able to recognise the category of unknown clothing in unconstrained and random configurations. We integrated the category recognition pipeline with a stereo vision system, clothing instance detection, and dual-arm manipulators to achieve an autonomous sorting system. To verify the performance of our proposed method, we build a high-resolution RGBD clothing dataset of 50 clothing items of 5 categories sampled in random configurations (a total of 2,100 clothing samples). Experimental results show that our approach is able to reach 83.2\% accuracy while classifying clothing items which were previously unseen during training. This advances beyond the previous state-of-the-art by 36.2\%. Finally, we evaluate the proposed approach in an autonomous robot sorting system, in which the robot recognises a clothing item from an unconstrained pile, grasps it, and sorts it into a box according to its category. Our proposed sorting system achieves reasonable sorting success rates with single-shot perception.

Single-Shot Clothing Category Recognition in Free-Configurations with Application to Autonomous Clothes Sorting

http://arxiv.org/pdf/1707.07157v1

Title SingleShot Clothing Category Recognition FreeConfigurations Application Autonomous Clothes Sorting Summary paper proposes singleshot approach recognising clothing category 25D feature propose two visual feature BSP BSpline Patch TSD Topology Spatial Distances task local BSP feature encoded LLC Localityconstrained Linear Coding fused three different global feature visual feature robust deformable shape approach able recognise category unknown clothing unconstrained random configuration integrated category recognition pipeline stereo vision system clothing instance detection dualarm manipulator achieve autonomous sorting system verify performance proposed method build highresolution RGBD clothing dataset 50 clothing item 5 category sampled random configuration total 2100 clothing sample Experimental result show approach able reach 832 accuracy classifying clothing item previously unseen training advance beyond previous stateoftheart 362 Finally evaluate proposed approach autonomous robot sorting system robot recognises clothing item unconstrained pile grasp sort box according category proposed sorting system achieves reasonable sorting success rate singleshot perception Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Juan Barrios-Avilés', 'Taras Iakymchuk', 'Jorge Samaniego', 'Alfredo Rosado-Muñoz']

1707.07188v1

This work develops a tracking system based on an event-based camera. A bioinspired filtering algorithm to reduce noise and transmitted data while keeping the main features at the scene is implemented in FPGA which also serves as a network node. POWERLINK IEEE 61158 industrial network is used to communicate the FPGA with a controller connected to a self-developed two axis servo-controlled robot. The FPGA includes the network protocol to integrate the event-based camera as any other existing network node. The inverse kinematics for the robot is included in the controller. In addition, another network node is used to control pneumatic valves blowing the ball at different speed and trajectories. To complete the system and provide a comparison, a traditional frame-based camera is also connected to the controller. The imaging data for the tracking system are obtained either from the event-based or frame-based camera. Results show that the robot can accurately follow the ball using fast image recognition, with the intrinsic advantages of the event-based system (size, price, power). This works shows how the development of new equipment and algorithms can be efficiently integrated in an industrial system, merging commercial industrial equipment with the new devices so that new technologies can rapidly enter into the industrial field.

An Event-based Fast Movement Detection Algorithm for a Positioning Robot Using POWERLINK Communication

http://arxiv.org/pdf/1707.07188v1

Title Eventbased Fast Movement Detection Algorithm Positioning Robot Using POWERLINK Communication Summary work develops tracking system based eventbased camera bioinspired filtering algorithm reduce noise transmitted data keeping main feature scene implemented FPGA also serf network node POWERLINK IEEE 61158 industrial network used communicate FPGA controller connected selfdeveloped two axis servocontrolled robot FPGA includes network protocol integrate eventbased camera existing network node inverse kinematics robot included controller addition another network node used control pneumatic valve blowing ball different speed trajectory complete system provide comparison traditional framebased camera also connected controller imaging data tracking system obtained either eventbased framebased camera Results show robot accurately follow ball using fast image recognition intrinsic advantage eventbased system size price power work show development new equipment algorithm efficiently integrated industrial system merging commercial industrial equipment new device new technology rapidly enter industrial 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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['Julian Zilly', 'Amit Boyarski', 'Micael Carvalho', 'Amir Atapour Abarghouei', 'Konstantinos Amplianitis', 'Aleksandr Krasnov', 'Massimiliano Mancini', 'Hernán Gonzalez', 'Riccardo Spezialetti', 'Carlos Sampedro Pérez', 'Hao Li']

1707.07210v1

The "digital Michelangelo project" was a seminal computer vision project in the early 2000's that pushed the capabilities of acquisition systems and involved multiple people from diverse fields, many of whom are now leaders in industry and academia. Reviewing this project with modern eyes provides us with the opportunity to reflect on several issues, relevant now as then to the field of computer vision and research in general, that go beyond the technical aspects of the work. This article was written in the context of a reading group competition at the week-long International Computer Vision Summer School 2017 (ICVSS) on Sicily, Italy. To deepen the participants understanding of computer vision and to foster a sense of community, various reading groups were tasked to highlight important lessons which may be learned from provided literature, going beyond the contents of the paper. This report is the winning entry of this guided discourse (Fig. 1). The authors closely examined the origins, fruits and most importantly lessons about research in general which may be distilled from the "digital Michelangelo project". Discussions leading to this report were held within the group as well as with Hao Li, the group mentor.

Inspiring Computer Vision System Solutions

http://arxiv.org/pdf/1707.07210v1

Title Inspiring Computer Vision System Solutions Summary digital Michelangelo project seminal computer vision project early 2000s pushed capability acquisition system involved multiple people diverse field many leader industry academia Reviewing project modern eye provides u opportunity reflect several issue relevant field computer vision research general go beyond technical aspect work article written context reading group competition weeklong International Computer Vision Summer School 2017 ICVSS Sicily Italy deepen participant understanding computer vision foster sense community various reading group tasked highlight important lesson may learned provided literature going beyond content paper report winning entry guided discourse Fig 1 author closely examined origin fruit importantly lesson research general may distilled digital Michelangelo project Discussions leading report held within group well Hao Li group mentor 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', 'Fabian Gouwens', 'Simon Jansen', 'Berend Küpers', 'Maurice Ramaker', 'Toon Goedemé']

1707.07244v1

This paper describes the vision based robotic picking system that was developed by our team, Team Applied Robotics, for the Amazon Picking Challenge 2016. This competition challenged teams to develop a robotic system that is able to pick a large variety of products from a shelve or a tote. We discuss the design considerations and our strategy, the high resolution 3D vision system, the use of a combination of texture and shape-based object detection algorithms, the robot path planning and object manipulators that were developed.

Team Applied Robotics: A closer look at our robotic picking system

http://arxiv.org/pdf/1707.07244v1

Title Team Applied Robotics closer look robotic picking system Summary paper describes vision based robotic picking system developed team Team Applied Robotics Amazon Picking Challenge 2016 competition challenged team develop robotic system able pick large variety product shelve tote discus design consideration strategy high resolution 3D vision system use combination texture shapebased object detection algorithm robot path planning object manipulator developed 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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['Kaylen J. Pfisterer', 'Robert Amelard', 'Audrey G. Chung', 'Alexander Wong']

1707.07312v2

Dysphagia affects 590 million people worldwide and increases risk for malnutrition. Pureed food may reduce choking, however preparation differences impact nutrient density making quality assurance necessary. This paper is the first study to investigate the feasibility of computational pureed food nutritional density analysis using an imaging system. Motivated by a theoretical optical dilution model, a novel deep neural network (DNN) was evaluated using 390 samples from thirteen types of commercially prepared purees at five dilutions. The DNN predicted relative concentration of the puree sample (20%, 40%, 60%, 80%, 100% initial concentration). Data were captured using same-side reflectance of multispectral imaging data at different polarizations at three exposures. Experimental results yielded an average top-1 prediction accuracy of 92.2+/-0.41% with sensitivity and specificity of 83.0+/-15.0% and 95.0+/-4.8%, respectively. This DNN imaging system for nutrient density analysis of pureed food shows promise as a novel tool for nutrient quality assurance.

A new take on measuring relative nutritional density: The feasibility of using a deep neural network to assess commercially-prepared pureed food concentrations

http://arxiv.org/pdf/1707.07312v2

Title new take measuring relative nutritional density feasibility using deep neural network ass commerciallyprepared pureed food concentration Summary Dysphagia affect 590 million people worldwide increase risk malnutrition Pureed food may reduce choking however preparation difference impact nutrient density making quality assurance necessary paper first study investigate feasibility computational pureed food nutritional density analysis using imaging system Motivated theoretical optical dilution model novel deep neural network DNN evaluated using 390 sample thirteen type commercially prepared puree five dilution DNN predicted relative concentration puree sample 20 40 60 80 100 initial concentration Data captured using sameside reflectance multispectral imaging data different polarization three exposure Experimental result yielded average top1 prediction accuracy 922041 sensitivity specificity 830150 95048 respectively DNN imaging system nutrient density analysis pureed food show promise novel tool nutrient quality assurance 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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['Shichao Yang', 'Yulan Huang', 'Sebastian Scherer']

1707.07388v1

Semantic 3D mapping can be used for many applications such as robot navigation and virtual interaction. In recent years, there has been great progress in semantic segmentation and geometric 3D mapping. However, it is still challenging to combine these two tasks for accurate and large-scale semantic mapping from images. In the paper, we propose an incremental and (near) real-time semantic mapping system. A 3D scrolling occupancy grid map is built to represent the world, which is memory and computationally efficient and bounded for large scale environments. We utilize the CNN segmentation as prior prediction and further optimize 3D grid labels through a novel CRF model. Superpixels are utilized to enforce smoothness and form robust P N high order potential. An efficient mean field inference is developed for the graph optimization. We evaluate our system on the KITTI dataset and improve the segmentation accuracy by 10% over existing systems.

Semantic 3D Occupancy Mapping through Efficient High Order CRFs

http://arxiv.org/pdf/1707.07388v1

Title Semantic 3D Occupancy Mapping Efficient High Order CRFs Summary Semantic 3D mapping used many application robot navigation virtual interaction recent year great progress semantic segmentation geometric 3D mapping However still challenging combine two task accurate largescale semantic mapping image paper propose incremental near realtime semantic mapping system 3D scrolling occupancy grid map built represent world memory computationally efficient bounded large scale environment utilize CNN segmentation prior prediction optimize 3D grid label novel CRF model Superpixels utilized enforce smoothness form robust P N high order potential efficient mean field inference developed graph optimization evaluate system KITTI dataset improve segmentation accuracy 10 existing 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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['Hongyoon Choi']

1707.07932v1

Autism spectrum disorder (ASD) is regarded as a brain disease with globally disrupted neuronal networks. Even though fMRI studies have revealed abnormal functional connectivity in ASD, they have not reached a consensus of the disrupted patterns. Here, a deep learning-based feature extraction method identifies multivariate and nonlinear functional connectivity patterns of ASD. Resting-state fMRI data of 972 subjects (465 ASD 507 normal controls) acquired from the Autism Brain Imaging Data Exchange were used. A functional connectivity matrix of each subject was generated using 90 predefined brain regions. As a data-driven feature extraction method without prior knowledge such as subjects diagnosis, variational autoencoder (VAE) summarized the functional connectivity matrix into 2 features. Those feature values of ASD patients were statistically compared with those of controls. A feature was significantly different between ASD and normal controls. The extracted features were visualized by VAE-based generator which can produce virtual functional connectivity matrices. The ASD-related feature was associated with frontoparietal connections, interconnections of the dorsal medial frontal cortex and corticostriatal connections. It also showed a trend of negative correlation with full-scale IQ. A data-driven feature extraction based on deep learning could identify complex patterns of functional connectivity of ASD. This approach will help discover complex patterns of abnormalities in brain connectivity in various brain disorders.

Functional connectivity patterns of autism spectrum disorder identified by deep feature learning

http://arxiv.org/pdf/1707.07932v1

Title Functional connectivity pattern autism spectrum disorder identified deep feature learning Summary Autism spectrum disorder ASD regarded brain disease globally disrupted neuronal network Even though fMRI study revealed abnormal functional connectivity ASD reached consensus disrupted pattern deep learningbased feature extraction method identifies multivariate nonlinear functional connectivity pattern ASD Restingstate fMRI data 972 subject 465 ASD 507 normal control acquired Autism Brain Imaging Data Exchange used functional connectivity matrix subject generated using 90 predefined brain region datadriven feature extraction method without prior knowledge subject diagnosis variational autoencoder VAE summarized functional connectivity matrix 2 feature feature value ASD patient statistically compared control feature significantly different ASD normal control extracted feature visualized VAEbased generator produce virtual functional connectivity matrix ASDrelated feature associated frontoparietal connection interconnection dorsal medial frontal cortex corticostriatal connection also showed trend negative correlation fullscale IQ datadriven feature extraction based deep learning could identify complex pattern functional connectivity ASD approach help discover complex pattern abnormality brain connectivity various brain disorder 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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['Seyed Amir Tafrishi', 'Vahid E. Kandjani']

1707.08095v1

This paper presents a state-of-the-art approach in object detection for being applied in future SLAM problems. Although, many SLAM methods are proposed to create suitable autonomy for mobile robots namely ground vehicles, they still face overconfidence and large computations during entrance to immense spaces with many landmarks. In particular, they suffer from impractical applications via sole reliance on the limited sensors like camera. Proposed method claims that unmanned ground vehicles without having huge amount of database for object definition and highly advance prediction parameters can deal with incoming objects during straight motion of camera in real-time. Line-Circle (LC) filter tries to apply detection, tracking and learning to each defined experts to obtain more information for judging scene without over-calculation. In this filter, circle expert let us summarize edges in groups. The Interactive feedback learning between each expert creates minimal error that fights against overwhelming landmark signs in crowded scenes without mapping. Our experts basically are dependent on trust factors' covariance with geometric definitions to ignore, emerge and compare detected landmarks. The experiment for validating the model is taken place utilizing a camera beside an IMU sensor for location estimation.

Line-Circle: A Geometric Filter for Single Camera Edge-Based Object Detection

http://arxiv.org/pdf/1707.08095v1

Title LineCircle Geometric Filter Single Camera EdgeBased Object Detection Summary paper present stateoftheart approach object detection applied future SLAM problem Although many SLAM method proposed create suitable autonomy mobile robot namely ground vehicle still face overconfidence large computation entrance immense space many landmark particular suffer impractical application via sole reliance limited sensor like camera Proposed method claim unmanned ground vehicle without huge amount database object definition highly advance prediction parameter deal incoming object straight motion camera realtime LineCircle LC filter try apply detection tracking learning defined expert obtain information judging scene without overcalculation filter circle expert let u summarize edge group Interactive feedback learning expert creates minimal error fight overwhelming landmark sign crowded scene without mapping expert basically dependent trust factor covariance geometric definition ignore emerge compare detected landmark experiment validating model taken place utilizing camera beside IMU sensor location estimation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Johanna Delanoy', 'Adrien Bousseau', 'Mathieu Aubry', 'Phillip Isola', 'Alexei A. Efros']

1707.08390v2

Sketch-based modeling strives to bring the ease and immediacy of drawing to the 3D world. However, while drawings are easy for humans to create, they are very challenging for computers to interpret due to their sparsity and ambiguity. We propose a data-driven approach that tackles this challenge by learning to reconstruct 3D shapes from one or more drawings. At the core of our approach is a deep convolutional neural network (CNN) that predicts occupancy of a voxel grid from a line drawing. This CNN provides us with an initial 3D reconstruction as soon as the user completes a single drawing of the desired shape. We complement this single-view network with an updater CNN that refines an existing prediction given a new drawing of the shape created from a novel viewpoint. A key advantage of our approach is that we can apply the updater iteratively to fuse information from an arbitrary number of viewpoints, without requiring explicit stroke correspondences between the drawings. We train both CNNs by rendering synthetic contour drawings from hand-modeled shape collections as well as from procedurally-generated abstract shapes. Finally, we integrate our CNNs in a minimal modeling interface that allows users to seamlessly draw an object, rotate it to see its 3D reconstruction, and refine it by re-drawing from another vantage point using the 3D reconstruction as guidance. The main strengths of our approach are its robustness to freehand bitmap drawings, its ability to adapt to different object categories, and the continuum it offers between single-view and multi-view sketch-based modeling.

What You Sketch Is What You Get: 3D Sketching using Multi-View Deep Volumetric Prediction

http://arxiv.org/pdf/1707.08390v2

Title Sketch Get 3D Sketching using MultiView Deep Volumetric Prediction Summary Sketchbased modeling strives bring ease immediacy drawing 3D world However drawing easy human create challenging computer interpret due sparsity ambiguity propose datadriven approach tackle challenge learning reconstruct 3D shape one drawing core approach deep convolutional neural network CNN predicts occupancy voxel grid line drawing CNN provides u initial 3D reconstruction soon user completes single drawing desired shape complement singleview network updater CNN refines existing prediction given new drawing shape created novel viewpoint key advantage approach apply updater iteratively fuse information arbitrary number viewpoint without requiring explicit stroke correspondence drawing train CNNs rendering synthetic contour drawing handmodeled shape collection well procedurallygenerated abstract shape Finally integrate CNNs minimal modeling interface allows user seamlessly draw object rotate see 3D reconstruction refine redrawing another vantage point using 3D reconstruction guidance main strength approach robustness freehand bitmap drawing ability adapt different object category continuum offer singleview multiview sketchbased modeling 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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['Jaya Prakash', 'Subhamoy Mandal', 'Daniel Razansky', 'Vasilis Ntziachristos']

1707.08391v1

Optoacoustic (photoacoustic) tomography reconstructs maps of the initial pressure rise induced by the absorption of light pulses in tissue. In practice, due to inaccurate assumptions in the forward model employed, noise and other experimental factors, the images often contain errors, occasionally manifested as negative values. We present optoacoustic tomography based on an entropy maximization algorithm that uses logarithmic regularization as a potent method for imparting non-negative image reconstruction. We experimentally investigate the performance achieved by the entropy maximization scheme on phantoms and in vivo samples. The findings demonstrate that the proposed scheme reconstructs physically relevant image values devoid of unwanted negative contrast, thus improving quantitative imaging performance.

Maximum entropy based non-negative optoacoustic tomographic image reconstruction

http://arxiv.org/pdf/1707.08391v1

Title Maximum entropy based nonnegative optoacoustic tomographic image reconstruction Summary Optoacoustic photoacoustic tomography reconstructs map initial pressure rise induced absorption light pulse tissue practice due inaccurate assumption forward model employed noise experimental factor image often contain error occasionally manifested negative value present optoacoustic tomography based entropy maximization algorithm us logarithmic regularization potent method imparting nonnegative image reconstruction experimentally investigate performance achieved entropy maximization scheme phantom vivo sample finding demonstrate proposed scheme reconstructs physically relevant image value devoid unwanted negative contrast thus improving quantitative imaging 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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['Saman Naderiparizi', 'Mehrdad Hessar', 'Vamsi Talla', 'Shyamnath Gollakota', 'Joshua R. Smith']

1707.08718v1

Wireless video streaming has traditionally been considered an extremely power-hungry operation. Existing approaches optimize the camera and communication modules individually to minimize their power consumption. However, the joint redesign and optimization of wireless communication as well as the camera is what that provides more power saving. We present an ultra-low-power wireless video streaming camera. To achieve this, we present a novel "analog" video backscatter technique that feeds analog pixels from the photo-diodes directly to the backscatter hardware, thereby eliminating power consuming hardware components such as ADCs and amplifiers. We prototype our wireless camera using off-the-shelf hardware and show that our design can stream video at up to 13 FPS and can operate up to a distance of 150 feet from the access point. Our COTS prototype consumes 2.36mW. Finally, to demonstrate the potential of our design, we built two proof-of-concept applications: video streaming for micro-robots and security cameras for face detection.

Ultra-low-power Wireless Streaming Cameras

http://arxiv.org/pdf/1707.08718v1

Title Ultralowpower Wireless Streaming Cameras Summary Wireless video streaming traditionally considered extremely powerhungry operation Existing approach optimize camera communication module individually minimize power consumption However joint redesign optimization wireless communication well camera provides power saving present ultralowpower wireless video streaming camera achieve present novel analog video backscatter technique feed analog pixel photodiodes directly backscatter hardware thereby eliminating power consuming hardware component ADCs amplifier prototype wireless camera using offtheshelf hardware show design stream video 13 FPS operate distance 150 foot access point COTS prototype consumes 236mW Finally demonstrate potential design built two proofofconcept application video streaming microrobots security camera face 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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['Max Lieblich', 'Lucas Van Meter']

1707.09332v1

We study the multiview moduli problems that arise in computer vision. We show that these moduli spaces are always smooth and irreducible, in both the calibrated and uncalibrated cases, for any number of views. We also show that these moduli spaces always embed in (diagram) Hilbert schemes, and that these embeddings are open immersions for more than four views. Our approach also yields a natural smooth cover of the classical variety of essential matrices that seems not to have appeared in the literature to date.

Two Hilbert schemes in computer vision

http://arxiv.org/pdf/1707.09332v1

Title Two Hilbert scheme computer vision Summary study multiview modulus problem arise computer vision show modulus space always smooth irreducible calibrated uncalibrated case number view also show modulus space always embed diagram Hilbert scheme embeddings open immersion four view approach also yield natural smooth cover classical variety essential matrix seems appeared literature date 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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['Hu Chen', 'Yi Zhang', 'Yunjin Chen', 'Junfeng Zhang', 'Weihua Zhang', 'Huaiqiaing Sun', 'Yang Lv', 'Peixi Liao', 'Jiliu Zhou', 'Ge Wang']

1707.09636v3

Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-view CT, tomosynthesis, interior tomography, and so on. To perform sparse-data CT, the iterative reconstruction commonly use regularizers in the CS framework. Currently, how to choose the parameters adaptively for regularization is a major open problem. In this paper, inspired by the idea of machine learning especially deep learning, we unfold a state-of-the-art "fields of experts" based iterative reconstruction scheme up to a number of iterations for data-driven training, construct a Learned Experts' Assessment-based Reconstruction Network ("LEARN") for sparse-data CT, and demonstrate the feasibility and merits of our LEARN network. The experimental results with our proposed LEARN network produces a competitive performance with the well-known Mayo Clinic Low-Dose Challenge Dataset relative to several state-of-the-art methods, in terms of artifact reduction, feature preservation, and computational speed. This is consistent to our insight that because all the regularization terms and parameters used in the iterative reconstruction are now learned from the training data, our LEARN network utilizes application-oriented knowledge more effectively and recovers underlying images more favorably than competing algorithms. Also, the number of layers in the LEARN network is only 12, reducing the computational complexity of typical iterative algorithms by orders of magnitude.

LEARN: Learned Experts' Assessment-based Reconstruction Network for Sparse-data CT

http://arxiv.org/pdf/1707.09636v3

Title LEARN Learned Experts Assessmentbased Reconstruction Network Sparsedata CT Summary Compressive sensing CS proved effective tomographic reconstruction sparsely collected data undersampled measurement practically important fewview CT tomosynthesis interior tomography perform sparsedata CT iterative reconstruction commonly use regularizers CS framework Currently choose parameter adaptively regularization major open problem paper inspired idea machine learning especially deep learning unfold stateoftheart field expert based iterative reconstruction scheme number iteration datadriven training construct Learned Experts Assessmentbased Reconstruction Network LEARN sparsedata CT demonstrate feasibility merit LEARN network experimental result proposed LEARN network produce competitive performance wellknown Mayo Clinic LowDose Challenge Dataset relative several stateoftheart method term artifact reduction feature preservation computational speed consistent insight regularization term parameter used iterative reconstruction learned training data LEARN network utilizes applicationoriented knowledge effectively recovers underlying image favorably competing algorithm Also number layer LEARN network 12 reducing computational complexity typical iterative algorithm order magnitude 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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['Endel Poder']

1707.09775v1

Deep convolutional neural networks follow roughly the architecture of biological visual systems, and have shown a performance comparable to human observers in object recognition tasks. In this study, I test a pre-trained deep neural network in some classic visual search tasks. The results reveal a qualitative difference from human performance. It appears that there is no difference between searches for simple features that pop out in experiments with humans, and for feature configurations that exhibit strict capacity limitations in human vision. Both types of stimuli reveal moderate capacity limitations in the neural network tested here.

Capacity limitations of visual search in deep convolutional neural network

http://arxiv.org/pdf/1707.09775v1

Title Capacity limitation visual search deep convolutional neural network Summary Deep convolutional neural network follow roughly architecture biological visual system shown performance comparable human observer object recognition task study test pretrained deep neural network classic visual search task result reveal qualitative difference human performance appears difference search simple feature pop experiment human feature configuration exhibit strict capacity limitation human vision type stimulus reveal moderate capacity limitation neural network tested 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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['Alexandre Bali']

1707.09839v1

For a monochrome layer $x$ of opacity $0\le o_x\le1 $ placed on another monochrome layer of opacity 1, the result given by the standard formula is $$\small\Pi\left({\bf C}_\varphi\right)=1+\sum_{n=1}^2\left(2-n-(-1)^no_{\chi(\varphi+1)}\right)\left(\chi(n+\varphi-1)-o_{\chi(n+\varphi-1)}\right),$$ the formula being of course explained in detail in this paper. We will eventually deduce a very simple theorem, generalize it and then see its validity with alternative formulas to this standard containing the same main properties here exposed.

Superposition de calques monochromes d'opacités variables

http://arxiv.org/pdf/1707.09839v1

Title Superposition de calque monochrome dopacités variable Summary monochrome layer x opacity 0le oxle1 placed another monochrome layer opacity 1 result given standard formula smallPileftbf Cvarphiright1sumn12left2n1nochivarphi1rightleftchinvarphi1ochinvarphi1right formula course explained detail paper eventually deduce simple theorem generalize see validity alternative formula standard containing main property exposed 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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['Valentin Peretroukhin', 'William Vega-Brown', 'Nicholas Roy', 'Jonathan Kelly']

1708.00171v2

Many algorithms in computer vision and robotics make strong assumptions about uncertainty, and rely on the validity of these assumptions to produce accurate and consistent state estimates. In practice, dynamic environments may degrade sensor performance in predictable ways that cannot be captured with static uncertainty parameters. In this paper, we employ fast nonparametric Bayesian inference techniques to more accurately model sensor uncertainty. By setting a prior on observation uncertainty, we derive a predictive robust estimator, and show how our model can be learned from sample images, both with and without knowledge of the motion used to generate the data. We validate our approach through Monte Carlo simulations, and report significant improvements in localization accuracy relative to a fixed noise model in several settings, including on synthetic data, the KITTI dataset, and our own experimental platform.

PROBE-GK: Predictive Robust Estimation using Generalized Kernels

http://arxiv.org/pdf/1708.00171v2

Title PROBEGK Predictive Robust Estimation using Generalized Kernels Summary Many algorithm computer vision robotics make strong assumption uncertainty rely validity assumption produce accurate consistent state estimate practice dynamic environment may degrade sensor performance predictable way cannot captured static uncertainty parameter paper employ fast nonparametric Bayesian inference technique accurately model sensor uncertainty setting prior observation uncertainty derive predictive robust estimator show model learned sample image without knowledge motion used generate data validate approach Monte Carlo simulation report significant improvement localization accuracy relative fixed noise model several setting including synthetic data KITTI dataset experimental platform 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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['Valentin Peretroukhin', 'Lee Clement', 'Matthew Giamou', 'Jonathan Kelly']

1708.00174v2

Navigation in unknown, chaotic environments continues to present a significant challenge for the robotics community. Lighting changes, self-similar textures, motion blur, and moving objects are all considerable stumbling blocks for state-of-the-art vision-based navigation algorithms. In this paper we present a novel technique for improving localization accuracy within a visual-inertial navigation system (VINS). We make use of training data to learn a model for the quality of visual features with respect to localization error in a given environment. This model maps each visual observation from a predefined prediction space of visual-inertial predictors onto a scalar weight, which is then used to scale the observation covariance matrix. In this way, our model can adjust the influence of each observation according to its quality. We discuss our choice of predictors and report substantial reductions in localization error on 4 km of data from the KITTI dataset, as well as on experimental datasets consisting of 700 m of indoor and outdoor driving on a small ground rover equipped with a Skybotix VI-Sensor.

PROBE: Predictive Robust Estimation for Visual-Inertial Navigation

http://arxiv.org/pdf/1708.00174v2

Title PROBE Predictive Robust Estimation VisualInertial Navigation Summary Navigation unknown chaotic environment continues present significant challenge robotics community Lighting change selfsimilar texture motion blur moving object considerable stumbling block stateoftheart visionbased navigation algorithm paper present novel technique improving localization accuracy within visualinertial navigation system VINS make use training data learn model quality visual feature respect localization error given environment model map visual observation predefined prediction space visualinertial predictor onto scalar weight used scale observation covariance matrix way model adjust influence observation according quality discus choice predictor report substantial reduction localization error 4 km data KITTI dataset well experimental datasets consisting 700 indoor outdoor driving small ground rover equipped Skybotix VISensor 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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['Hyung Suk Park', 'Sung Min Lee', 'Hwa Pyung Kim', 'Jin Keun Seo']

1708.00607v2

This paper proposes a sinogram consistency learning method to deal with beam-hardening related artifacts in polychromatic computerized tomography (CT). The presence of highly attenuating materials in the scan field causes an inconsistent sinogram, that does not match the range space of the Radon transform. When the mismatched data are entered into the range space during CT reconstruction, streaking and shading artifacts are generated owing to the inherent nature of the inverse Radon transform. The proposed learning method aims to repair inconsistent sinograms by removing the primary metal-induced beam-hardening factors along the metal trace in the sinogram. Taking account of the fundamental difficulty in obtaining sufficient training data in a medical environment, the learning method is designed to use simulated training data and a patient-type specific learning model is used to simplify the learning process. The feasibility of the proposed method is investigated using a dataset, consisting of real CT scan of pelvises containing hip prostheses. The anatomical areas in training and test data are different, in order to demonstrate that the proposed method extracts the beam hardening features, selectively. The results show that our method successfully corrects sinogram inconsistency by extracting beam-hardening sources by means of deep learning. This paper proposed a deep learning method of sinogram correction for beam hardening reduction in CT for the first time. Conventional methods for beam hardening reduction are based on regularizations, and have the fundamental drawback of being not easily able to use manifold CT images, while a deep learning approach has the potential to do so.

CT sinogram-consistency learning for metal-induced beam hardening correction

http://arxiv.org/pdf/1708.00607v2

Title CT sinogramconsistency learning metalinduced beam hardening correction Summary paper proposes sinogram consistency learning method deal beamhardening related artifact polychromatic computerized tomography CT presence highly attenuating material scan field cause inconsistent sinogram match range space Radon transform mismatched data entered range space CT reconstruction streaking shading artifact generated owing inherent nature inverse Radon transform proposed learning method aim repair inconsistent sinograms removing primary metalinduced beamhardening factor along metal trace sinogram Taking account fundamental difficulty obtaining sufficient training data medical environment learning method designed use simulated training data patienttype specific learning model used simplify learning process feasibility proposed method investigated using dataset consisting real CT scan pelvis containing hip prosthesis anatomical area training test data different order demonstrate proposed method extract beam hardening feature selectively result show method successfully corrects sinogram inconsistency extracting beamhardening source mean deep learning paper proposed deep learning method sinogram correction beam hardening reduction CT first time Conventional method beam hardening reduction based regularization fundamental drawback easily able use manifold CT image deep learning approach potential 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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['Jae Sung Park', 'Nam Ik Cho']

1708.00636v1

This paper presents an algorithm that enhances undesirably illuminated images by generating and fusing multi-level illuminations from a single image.The input image is first decomposed into illumination and reflectance components by using an edge-preserving smoothing filter. Then the reflectance component is scaled up to improve the image details in bright areas. The illumination component is scaled up and down to generate several illumination images that correspond to certain camera exposure values different from the original. The virtual multi-exposure illuminations are blended into an enhanced illumination, where we also propose a method to generate appropriate weight maps for the tone fusion. Finally, an enhanced image is obtained by multiplying the equalized illumination and enhanced reflectance. Experiments show that the proposed algorithm produces visually pleasing output and also yields comparable objective results to the conventional enhancement methods, while requiring modest computational loads.

Generation of High Dynamic Range Illumination from a Single Image for the Enhancement of Undesirably Illuminated Images

http://arxiv.org/pdf/1708.00636v1

Title Generation High Dynamic Range Illumination Single Image Enhancement Undesirably Illuminated Images Summary paper present algorithm enhances undesirably illuminated image generating fusing multilevel illumination single imageThe input image first decomposed illumination reflectance component using edgepreserving smoothing filter reflectance component scaled improve image detail bright area illumination component scaled generate several illumination image correspond certain camera exposure value different original virtual multiexposure illumination blended enhanced illumination also propose method generate appropriate weight map tone fusion Finally enhanced image obtained multiplying equalized illumination enhanced reflectance Experiments show proposed algorithm produce visually pleasing output also yield comparable objective result conventional enhancement method requiring modest computational load 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 Vasquez', 'Marina Kollmitz', 'Andreas Eitel', 'Wolfram Burgard']

1708.00674v1

Robots operating in populated environments encounter many different types of people, some of whom might have an advanced need for cautious interaction, because of physical impairments or their advanced age. Robots therefore need to recognize such advanced demands to provide appropriate assistance, guidance or other forms of support. In this paper, we propose a depth-based perception pipeline that estimates the position and velocity of people in the environment and categorizes them according to the mobility aids they use: pedestrian, person in wheelchair, person in a wheelchair with a person pushing them, person with crutches and person using a walker. We present a fast region proposal method that feeds a Region-based Convolutional Network (Fast R-CNN). With this, we speed up the object detection process by a factor of seven compared to a dense sliding window approach. We furthermore propose a probabilistic position, velocity and class estimator to smooth the CNN's detections and account for occlusions and misclassifications. In addition, we introduce a new hospital dataset with over 17,000 annotated RGB-D images. Extensive experiments confirm that our pipeline successfully keeps track of people and their mobility aids, even in challenging situations with multiple people from different categories and frequent occlusions. Videos of our experiments and the dataset are available at http://www2.informatik.uni-freiburg.de/~kollmitz/MobilityAids

Deep Detection of People and their Mobility Aids for a Hospital Robot

http://arxiv.org/pdf/1708.00674v1

Title Deep Detection People Mobility Aids Hospital Robot Summary Robots operating populated environment encounter many different type people might advanced need cautious interaction physical impairment advanced age Robots therefore need recognize advanced demand provide appropriate assistance guidance form support paper propose depthbased perception pipeline estimate position velocity people environment categorizes according mobility aid use pedestrian person wheelchair person wheelchair person pushing person crutch person using walker present fast region proposal method feed Regionbased Convolutional Network Fast RCNN speed object detection process factor seven compared dense sliding window approach furthermore propose probabilistic position velocity class estimator smooth CNNs detection account occlusion misclassifications addition introduce new hospital dataset 17000 annotated RGBD image Extensive experiment confirm pipeline successfully keep track people mobility aid even challenging situation multiple people different category frequent occlusion Videos experiment dataset available httpwww2informatikunifreiburgdekollmitzMobilityAids 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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['Gjorgji Strezoski', 'Marcel Worring']

1708.00684v1

Vast amounts of artistic data is scattered on-line from both museums and art applications. Collecting, processing and studying it with respect to all accompanying attributes is an expensive process. With a motivation to speed up and improve the quality of categorical analysis in the artistic domain, in this paper we propose an efficient and accurate method for multi-task learning with a shared representation applied in the artistic domain. We continue to show how different multi-task configurations of our method behave on artistic data and outperform handcrafted feature approaches as well as convolutional neural networks. In addition to the method and analysis, we propose a challenge like nature to the new aggregated data set with almost half a million samples and structured meta-data to encourage further research and societal engagement.

OmniArt: Multi-task Deep Learning for Artistic Data Analysis

http://arxiv.org/pdf/1708.00684v1

Title OmniArt Multitask Deep Learning Artistic Data Analysis Summary Vast amount artistic data scattered online museum art application Collecting processing studying respect accompanying attribute expensive process motivation speed improve quality categorical analysis artistic domain paper propose efficient accurate method multitask learning shared representation applied artistic domain continue show different multitask configuration method behave artistic data outperform handcrafted feature approach well convolutional neural network addition method analysis propose challenge like nature new aggregated data set almost half million sample structured metadata encourage research societal engagement 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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['Abhinav Gupta', 'Agrim Khanna', 'Anmol Jagetia', 'Devansh Sharma', 'Sanchit Alekh', 'Vaibhav Choudhary']

1708.00931v1

The massive explosion and ubiquity of computing devices and the outreach of the web have been the most defining events of the century so far. As more and more people gain access to the internet, traditional know-something and have-something authentication methods such as PINs and passwords are proving to be insufficient for prohibiting unauthorized access to increasingly personal data on the web. Therefore, the need of the hour is a user-verification system that is not only more reliable and secure, but also unobtrusive and minimalistic. Keystroke Dynamics is a novel Biometric Technique; it is not only unobtrusive, but also transparent and inexpensive. The fusion of keystroke dynamics and Face Recognition engenders the most desirable characteristics of a verification system. Our implementation uses Hidden Markov Models (HMM) for modelling the Keystroke Dynamics, with the help of two widely used Feature Vectors: Keypress Latency and Keypress Duration. On the other hand, Face Recognition makes use of the traditional Eigenfaces approach.The results show that the system has a high precision, with a False Acceptance Rate of 5.4% and a False Rejection Rate of 9.2%. Moreover, it is also future-proof, as the hardware requirements, i.e. camera and keyboard (physical or on-screen), have become an indispensable part of modern computing.

Combining Keystroke Dynamics and Face Recognition for User Verification

http://arxiv.org/pdf/1708.00931v1

Title Combining Keystroke Dynamics Face Recognition User Verification Summary massive explosion ubiquity computing device outreach web defining event century far people gain access internet traditional knowsomething havesomething authentication method PINs password proving insufficient prohibiting unauthorized access increasingly personal data web Therefore need hour userverification system reliable secure also unobtrusive minimalistic Keystroke Dynamics novel Biometric Technique unobtrusive also transparent inexpensive fusion keystroke dynamic Face Recognition engenders desirable characteristic verification system implementation us Hidden Markov Models HMM modelling Keystroke Dynamics help two widely used Feature Vectors Keypress Latency Keypress Duration hand Face Recognition make use traditional Eigenfaces approachThe result show system high precision False Acceptance Rate 54 False Rejection Rate 92 Moreover also futureproof hardware requirement ie camera keyboard physical onscreen become indispensable part modern computing 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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['Bryan Willimon', 'Steven Hickson', 'Ian Walker', 'Stan Birchfield']

1708.00940v1

We propose an algorithm that uses energy mini- mization to estimate the current configuration of a non-rigid object. Our approach utilizes an RGBD image to calculate corresponding SURF features, depth, and boundary informa- tion. We do not use predetermined features, thus enabling our system to operate on unmodified objects. Our approach relies on a 3D nonlinear energy minimization framework to solve for the configuration using a semi-implicit scheme. Results show various scenarios of dynamic posters and shirts in different configurations to illustrate the performance of the method. In particular, we show that our method is able to estimate the configuration of a textureless nonrigid object with no correspondences available.

An Energy Minimization Approach to 3D Non-Rigid Deformable Surface Estimation Using RGBD Data

http://arxiv.org/pdf/1708.00940v1

Title Energy Minimization Approach 3D NonRigid Deformable Surface Estimation Using RGBD Data Summary propose algorithm us energy mini mization estimate current configuration nonrigid object approach utilizes RGBD image calculate corresponding SURF feature depth boundary informa tion use predetermined feature thus enabling system operate unmodified object approach relies 3D nonlinear energy minimization framework solve configuration using semiimplicit scheme Results show various scenario dynamic poster shirt different configuration illustrate performance method particular show method able estimate configuration textureless nonrigid object correspondence available 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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['Junnan Li', 'Yongkang Wong', 'Qi Zhao', 'Mohan Kankanhalli']

1708.00973v1

Training deep learning based video classifiers for action recognition requires a large amount of labeled videos. The labeling process is labor-intensive and time-consuming. On the other hand, large amount of weakly-labeled images are uploaded to the Internet by users everyday. To harness the rich and highly diverse set of Web images, a scalable approach is to crawl these images to train deep learning based classifier, such as Convolutional Neural Networks (CNN). However, due to the domain shift problem, the performance of Web images trained deep classifiers tend to degrade when directly deployed to videos. One way to address this problem is to fine-tune the trained models on videos, but sufficient amount of annotated videos are still required. In this work, we propose a novel approach to transfer knowledge from image domain to video domain. The proposed method can adapt to the target domain (i.e. video data) with limited amount of training data. Our method maps the video frames into a low-dimensional feature space using the class-discriminative spatial attention map for CNNs. We design a novel Siamese EnergyNet structure to learn energy functions on the attention maps by jointly optimizing two loss functions, such that the attention map corresponding to a ground truth concept would have higher energy. We conduct extensive experiments on two challenging video recognition datasets (i.e. TVHI and UCF101), and demonstrate the efficacy of our proposed method.

Attention Transfer from Web Images for Video Recognition

http://arxiv.org/pdf/1708.00973v1

Title Attention Transfer Web Images Video Recognition Summary Training deep learning based video classifier action recognition requires large amount labeled video labeling process laborintensive timeconsuming hand large amount weaklylabeled image uploaded Internet user everyday harness rich highly diverse set Web image scalable approach crawl image train deep learning based classifier Convolutional Neural Networks CNN However due domain shift problem performance Web image trained deep classifier tend degrade directly deployed video One way address problem finetune trained model video sufficient amount annotated video still required work propose novel approach transfer knowledge image domain video domain proposed method adapt target domain ie video data limited amount training data method map video frame lowdimensional feature space using classdiscriminative spatial attention map CNNs design novel Siamese EnergyNet structure learn energy function attention map jointly optimizing two loss function attention map corresponding ground truth concept would higher energy conduct extensive experiment two challenging video recognition datasets ie TVHI UCF101 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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['Shaghayegh Kargozar Nahavandya', 'Lalit Kumar', 'Pedram Ghamisi']

1708.01089v1

The SLEUTH model, based on the Cellular Automata (CA), can be applied to city development simulation in metropolitan areas. In this study the SLEUTH model was used to model the urban expansion and predict the future possible behavior of the urban growth in Tehran. The fundamental data were five Landsat TM and ETM images of 1988, 1992, 1998, 2001 and 2010. Three scenarios were designed to simulate the spatial pattern. The first scenario assumed historical urbanization mode would persist and the only limitations for development were height and slope. The second one was a compact scenario which makes the growth mostly internal and limited the expansion of suburban areas. The last scenario proposed a polycentric urban structure which let the little patches grow without any limitation and would not consider the areas beyond the specific buffer zone from the larger patches for development. Results showed that the urban growth rate was greater in the first scenario in comparison with the other two scenarios. Also it was shown that the third scenario was more suitable for Tehran since it could avoid undesirable effects such as congestion and pollution and was more in accordance with the conditions of Tehran city.

Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Tehran metropolitan area in Iran

http://arxiv.org/pdf/1708.01089v1

Title Using SLEUTH urban growth model simulate impact future policy scenario urban land use Tehran metropolitan area Iran Summary SLEUTH model based Cellular Automata CA applied city development simulation metropolitan area study SLEUTH model used model urban expansion predict future possible behavior urban growth Tehran fundamental data five Landsat TM ETM image 1988 1992 1998 2001 2010 Three scenario designed simulate spatial pattern first scenario assumed historical urbanization mode would persist limitation development height slope second one compact scenario make growth mostly internal limited expansion suburban area last scenario proposed polycentric urban structure let little patch grow without limitation would consider area beyond specific buffer zone larger patch development Results showed urban growth rate greater first scenario comparison two scenario Also shown third scenario suitable Tehran since could avoid undesirable effect congestion pollution accordance condition Tehran city 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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['Casey J. Pellizzari', 'Mark F. Spencer', 'Charles A. Bouman']

1708.01142v1

The estimation of phase errors from digital-holography data is critical for applications such as imaging or wave-front sensing. Conventional techniques require multiple i.i.d. data and perform poorly in the presence of high noise or large phase errors. In this paper we propose a method to estimate isoplanatic phase errors from a single data realization. We develop a model-based iterative reconstruction algorithm which computes the maximum a posteriori estimate of the phase and the speckle-free object reflectance. Using simulated data, we show that the algorithm is robust against high noise and strong phase errors.

Phase-error estimation and image reconstruction from digital-holography data using a Bayesian framework

http://arxiv.org/pdf/1708.01142v1

Title Phaseerror estimation image reconstruction digitalholography data using Bayesian framework Summary estimation phase error digitalholography data critical application imaging wavefront sensing Conventional technique require multiple iid data perform poorly presence high noise large phase error paper propose method estimate isoplanatic phase error single data realization develop modelbased iterative reconstruction algorithm computes maximum posteriori estimate phase specklefree object reflectance Using simulated data show algorithm robust high noise strong phase error 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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['Ariel Ephrat', 'Tavi Halperin', 'Shmuel Peleg']

1708.01204v3

Speechreading is the task of inferring phonetic information from visually observed articulatory facial movements, and is a notoriously difficult task for humans to perform. In this paper we present an end-to-end model based on a convolutional neural network (CNN) for generating an intelligible and natural-sounding acoustic speech signal from silent video frames of a speaking person. We train our model on speakers from the GRID and TCD-TIMIT datasets, and evaluate the quality and intelligibility of reconstructed speech using common objective measurements. We show that speech predictions from the proposed model attain scores which indicate significantly improved quality over existing models. In addition, we show promising results towards reconstructing speech from an unconstrained dictionary.

Improved Speech Reconstruction from Silent Video

http://arxiv.org/pdf/1708.01204v3

Title Improved Speech Reconstruction Silent Video Summary Speechreading task inferring phonetic information visually observed articulatory facial movement notoriously difficult task human perform paper present endtoend model based convolutional neural network CNN generating intelligible naturalsounding acoustic speech signal silent video frame speaking person train model speaker GRID TCDTIMIT datasets evaluate quality intelligibility reconstructed speech using common objective measurement show speech prediction proposed model attain score indicate significantly improved quality existing model addition show promising result towards reconstructing speech unconstrained dictionary 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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['Long Chen', 'Karl Francis', 'Wen Tang']

1708.01208v3

In Augmented Reality (AR) environment, realistic interactions between the virtual and real objects play a crucial role in user experience. Much of recent advances in AR has been largely focused on developing geometry-aware environment, but little has been done in dealing with interactions at the semantic level. High-level scene understanding and semantic descriptions in AR would allow effective design of complex applications and enhanced user experience. In this paper, we present a novel approach and a prototype system that enables the deeper understanding of semantic properties of the real world environment, so that realistic physical interactions between the real and the virtual objects can be generated. A material-aware AR environment has been created based on the deep material learning using a fully convolutional network (FCN). The state-of-the-art dense Simultaneous Localisation and Mapping (SLAM) has been used for the semantic mapping. Together with efficient accelerated 3D ray casting, natural and realistic physical interactions are generated for interactive AR games. Our approach has significant impact on the future development of advanced AR systems and applications.

Semantic Augmented Reality Environment with Material-Aware Physical Interactions

http://arxiv.org/pdf/1708.01208v3

Title Semantic Augmented Reality Environment MaterialAware Physical Interactions Summary Augmented Reality AR environment realistic interaction virtual real object play crucial role user experience Much recent advance AR largely focused developing geometryaware environment little done dealing interaction semantic level Highlevel scene understanding semantic description AR would allow effective design complex application enhanced user experience paper present novel approach prototype system enables deeper understanding semantic property real world environment realistic physical interaction real virtual object generated materialaware AR environment created based deep material learning using fully convolutional network FCN stateoftheart dense Simultaneous Localisation Mapping SLAM used semantic mapping Together efficient accelerated 3D ray casting natural realistic physical interaction generated interactive AR game approach significant impact future development advanced AR system 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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['Yury Korolev', 'Jan Lellmann']

1708.01244v3

We present and analyse an approach to image reconstruction problems with imperfect forward models based on partially ordered spaces - Banach lattices. In this approach, errors in the data and in the forward models are described using order intervals. The method can be characterised as the lattice analogue of the residual method, where the feasible set is defined by linear inequality constraints. The study of this feasible set is the main contribution of this paper. Convexity of this feasible set is examined in several settings and modifications for introducing additional information about the forward operator are considered. Numerical examples demonstrate the performance of the method in deblurring with errors in the blurring kernel.

Image reconstruction with imperfect forward models and applications in deblurring

http://arxiv.org/pdf/1708.01244v3

Title Image reconstruction imperfect forward model application deblurring Summary present analyse approach image reconstruction problem imperfect forward model based partially ordered space Banach lattice approach error data forward model described using order interval method characterised lattice analogue residual method feasible set defined linear inequality constraint study feasible set main contribution paper Convexity feasible set examined several setting modification introducing additional information forward operator considered Numerical example demonstrate performance method deblurring error blurring kernel 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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['Eric Jonas', 'Monica G. Bobra', 'Vaishaal Shankar', 'J. Todd Hoeksema', 'Benjamin Recht']

1708.01323v1

The precise physical process that triggers solar flares is not currently understood. Here we attempt to capture the signature of this mechanism in solar image data of various wavelengths and use these signatures to predict flaring activity. We do this by developing an algorithm that [1] automatically generates features in 5.5 TB of image data taken by the Solar Dynamics Observatory of the solar photosphere, chromosphere, transition region, and corona during the time period between May 2010 and May 2014, [2] combines these features with other features based on flaring history and a physical understanding of putative flaring processes, and [3] classifies these features to predict whether a solar active region will flare within a time period of $T$ hours, where $T$ = 2 and 24. We find that when optimizing for the True Skill Score (TSS), photospheric vector magnetic field data combined with flaring history yields the best performance, and when optimizing for the area under the precision-recall curve, all the data are helpful. Our model performance yields a TSS of $0.84 \pm 0.03$ and $0.81 \pm 0.03$ in the $T$ = 2 and 24 hour cases, respectively, and a value of $0.13 \pm 0.07$ and $0.43 \pm 0.08$ for the area under the precision-recall curve in the $T$ = 2 and 24 hour cases, respectively. These relatively high scores are similar to, but not greater than, other attempts to predict solar flares. Given the similar values of algorithm performance across various types of models reported in the literature, we conclude that we can expect a certain baseline predictive capacity using these data. This is the first attempt to predict solar flares using photospheric vector magnetic field data as well as multiple wavelengths of image data from the chromosphere, transition region, and corona.

Flare Prediction Using Photospheric and Coronal Image Data

http://arxiv.org/pdf/1708.01323v1

Title Flare Prediction Using Photospheric Coronal Image Data Summary precise physical process trigger solar flare currently understood attempt capture signature mechanism solar image data various wavelength use signature predict flaring activity developing algorithm 1 automatically generates feature 55 TB image data taken Solar Dynamics Observatory solar photosphere chromosphere transition region corona time period May 2010 May 2014 2 combine feature feature based flaring history physical understanding putative flaring process 3 classifies feature predict whether solar active region flare within time period hour 2 24 find optimizing True Skill Score TSS photospheric vector magnetic field data combined flaring history yield best performance optimizing area precisionrecall curve data helpful model performance yield TSS 084 pm 003 081 pm 003 2 24 hour case respectively value 013 pm 007 043 pm 008 area precisionrecall curve 2 24 hour case respectively relatively high score similar greater attempt predict solar flare Given similar value algorithm performance across various type model reported literature conclude expect certain baseline predictive capacity using data first attempt predict solar flare using photospheric vector magnetic field data well multiple wavelength image data chromosphere transition region corona 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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['Ori Ganoni', 'Ramakrishnan Mukundan']

1708.01938v1

This paper presents a generalized framework for the simulation of multiple robots and drones in highly realistic models of natural environments. The proposed simulation architecture uses the Unreal Engine4 for generating both optical and depth sensor outputs from any position and orientation within the environment and provides several key domain specific simulation capabilities. Various components and functionalities of the system have been discussed in detail. The simulation engine also allows users to test and validate a wide range of computer vision algorithms involving different drone configurations under many types of environmental effects such as wind gusts. The paper demonstrates the effectiveness of the system by giving experimental results for a test scenario where one drone tracks the simulated motion of another in a complex natural environment.

A Framework for Visually Realistic Multi-robot Simulation in Natural Environment

http://arxiv.org/pdf/1708.01938v1

Title Framework Visually Realistic Multirobot Simulation Natural Environment Summary paper present generalized framework simulation multiple robot drone highly realistic model natural environment proposed simulation architecture us Unreal Engine4 generating optical depth sensor output position orientation within environment provides several key domain specific simulation capability Various component functionality system discussed detail simulation engine also allows user test validate wide range computer vision algorithm involving different drone configuration many type environmental effect wind gust paper demonstrates effectiveness system giving experimental result test scenario one drone track simulated motion another complex natural environment Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Weipeng Xu', 'Avishek Chatterjee', 'Michael Zollhöfer', 'Helge Rhodin', 'Dushyant Mehta', 'Hans-Peter Seidel', 'Christian Theobalt']

1708.02136v2

We present the first marker-less approach for temporally coherent 3D performance capture of a human with general clothing from monocular video. Our approach reconstructs articulated human skeleton motion as well as medium-scale non-rigid surface deformations in general scenes. Human performance capture is a challenging problem due to the large range of articulation, potentially fast motion, and considerable non-rigid deformations, even from multi-view data. Reconstruction from monocular video alone is drastically more challenging, since strong occlusions and the inherent depth ambiguity lead to a highly ill-posed reconstruction problem. We tackle these challenges by a novel approach that employs sparse 2D and 3D human pose detections from a convolutional neural network using a batch-based pose estimation strategy. Joint recovery of per-batch motion allows to resolve the ambiguities of the monocular reconstruction problem based on a low dimensional trajectory subspace. In addition, we propose refinement of the surface geometry based on fully automatically extracted silhouettes to enable medium-scale non-rigid alignment. We demonstrate state-of-the-art performance capture results that enable exciting applications such as video editing and free viewpoint video, previously infeasible from monocular video. Our qualitative and quantitative evaluation demonstrates that our approach significantly outperforms previous monocular methods in terms of accuracy, robustness and scene complexity that can be handled.

MonoPerfCap: Human Performance Capture from Monocular Video

http://arxiv.org/pdf/1708.02136v2

Title MonoPerfCap Human Performance Capture Monocular Video Summary present first markerless approach temporally coherent 3D performance capture human general clothing monocular video approach reconstructs articulated human skeleton motion well mediumscale nonrigid surface deformation general scene Human performance capture challenging problem due large range articulation potentially fast motion considerable nonrigid deformation even multiview data Reconstruction monocular video alone drastically challenging since strong occlusion inherent depth ambiguity lead highly illposed reconstruction problem tackle challenge novel approach employ sparse 2D 3D human pose detection convolutional neural network using batchbased pose estimation strategy Joint recovery perbatch motion allows resolve ambiguity monocular reconstruction problem based low dimensional trajectory subspace addition propose refinement surface geometry based fully automatically extracted silhouette enable mediumscale nonrigid alignment demonstrate stateoftheart performance capture result enable exciting application video editing free viewpoint video previously infeasible monocular video qualitative quantitative evaluation demonstrates approach significantly outperforms previous monocular method term accuracy robustness scene complexity handled 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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['Jérémy Taquet', 'Gaël Écorchard', 'Libor Přeučil']

1708.02283v1

In a robotised warehouse a major issue is the safety of human operators in case of intervention in the work area of the robots. The current solution is to shut down every robot but it causes a loss of productivity, especially for large robotised warehouses. In order to avoid this loss we need to ensure the operator's security during his/her intervention in the warehouse without powering off the robots. The human operator needs to be localised in the warehouse and the trajectories of the robots have to be modified so that they do not interfere with the human. The purpose of this paper is to demonstrate a visual localisation method with visual elements that are already available in the current warehouse setup.

Real-Time Visual Localisation in a Tagged Environment

http://arxiv.org/pdf/1708.02283v1

Title RealTime Visual Localisation Tagged Environment Summary robotised warehouse major issue safety human operator case intervention work area robot current solution shut every robot cause loss productivity especially large robotised warehouse order avoid loss need ensure operator security hisher intervention warehouse without powering robot human operator need localised warehouse trajectory robot modified interfere human purpose paper demonstrate visual localisation method visual element already available current warehouse setup 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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['Jeffrey Hawke', 'Alex Bewley', 'Ingmar Posner']

1708.02330v1

This paper is about enabling robots to improve their perceptual performance through repeated use in their operating environment, creating local expert detectors fitted to the places through which a robot moves. We leverage the concept of 'experiences' in visual perception for robotics, accounting for bias in the data a robot sees by fitting object detector models to a particular place. The key question we seek to answer in this paper is simply: how do we define a place? We build bespoke pedestrian detector models for autonomous driving, highlighting the necessary trade off between generalisation and model capacity as we vary the extent of the place we fit to. We demonstrate a sizeable performance gain over a current state-of-the-art detector when using computationally lightweight bespoke place-fitted detector models.

What Makes a Place? Building Bespoke Place Dependent Object Detectors for Robotics

http://arxiv.org/pdf/1708.02330v1

Title Makes Place Building Bespoke Place Dependent Object Detectors Robotics Summary paper enabling robot improve perceptual performance repeated use operating environment creating local expert detector fitted place robot move leverage concept experience visual perception robotics accounting bias data robot see fitting object detector model particular place key question seek answer paper simply define place build bespoke pedestrian detector model autonomous driving highlighting necessary trade generalisation model capacity vary extent place fit demonstrate sizeable performance gain current stateoftheart detector using computationally lightweight bespoke placefitted detector 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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['Davy Neven', 'Bert De Brabandere', 'Stamatios Georgoulis', 'Marc Proesmans', 'Luc Van Gool']

1708.02550v1

Most approaches for instance-aware semantic labeling traditionally focus on accuracy. Other aspects like runtime and memory footprint are arguably as important for real-time applications such as autonomous driving. Motivated by this observation and inspired by recent works that tackle multiple tasks with a single integrated architecture, in this paper we present a real-time efficient implementation based on ENet that solves three autonomous driving related tasks at once: semantic scene segmentation, instance segmentation and monocular depth estimation. Our approach builds upon a branched ENet architecture with a shared encoder but different decoder branches for each of the three tasks. The presented method can run at 21 fps at a resolution of 1024x512 on the Cityscapes dataset without sacrificing accuracy compared to running each task separately.

Fast Scene Understanding for Autonomous Driving

http://arxiv.org/pdf/1708.02550v1

Title Fast Scene Understanding Autonomous Driving Summary approach instanceaware semantic labeling traditionally focus accuracy aspect like runtime memory footprint arguably important realtime application autonomous driving Motivated observation inspired recent work tackle multiple task single integrated architecture paper present realtime efficient implementation based ENet solves three autonomous driving related task semantic scene segmentation instance segmentation monocular depth estimation approach build upon branched ENet architecture shared encoder different decoder branch three task presented method run 21 fps resolution 1024x512 Cityscapes dataset without sacrificing accuracy compared running task separately Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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-0.047820236533880234, -0.02432696893811226, -0.08515117317438126, 0.013118311762809753, -0.016035228967666626]

['Bert De Brabandere', 'Davy Neven', 'Luc Van Gool']

1708.02551v1

Semantic instance segmentation remains a challenging task. In this work we propose to tackle the problem with a discriminative loss function, operating at the pixel level, that encourages a convolutional network to produce a representation of the image that can easily be clustered into instances with a simple post-processing step. The loss function encourages the network to map each pixel to a point in feature space so that pixels belonging to the same instance lie close together while different instances are separated by a wide margin. Our approach of combining an off-the-shelf network with a principled loss function inspired by a metric learning objective is conceptually simple and distinct from recent efforts in instance segmentation. In contrast to previous works, our method does not rely on object proposals or recurrent mechanisms. A key contribution of our work is to demonstrate that such a simple setup without bells and whistles is effective and can perform on par with more complex methods. Moreover, we show that it does not suffer from some of the limitations of the popular detect-and-segment approaches. We achieve competitive performance on the Cityscapes and CVPPP leaf segmentation benchmarks.

Semantic Instance Segmentation with a Discriminative Loss Function

http://arxiv.org/pdf/1708.02551v1

Title Semantic Instance Segmentation Discriminative Loss Function Summary Semantic instance segmentation remains challenging task work propose tackle problem discriminative loss function operating pixel level encourages convolutional network produce representation image easily clustered instance simple postprocessing step loss function encourages network map pixel point feature space pixel belonging instance lie close together different instance separated wide margin approach combining offtheshelf network principled loss function inspired metric learning objective conceptually simple distinct recent effort instance segmentation contrast previous work method rely object proposal recurrent mechanism key contribution work demonstrate simple setup without bell whistle effective perform par complex method Moreover show suffer limitation popular detectandsegment approach achieve competitive performance Cityscapes CVPPP leaf segmentation benchmark 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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['Zoya Bylinskii', 'Nam Wook Kim', "Peter O'Donovan", 'Sami Alsheikh', 'Spandan Madan', 'Hanspeter Pfister', 'Fredo Durand', 'Bryan Russell', 'Aaron Hertzmann']

1708.02660v1

Knowing where people look and click on visual designs can provide clues about how the designs are perceived, and where the most important or relevant content lies. The most important content of a visual design can be used for effective summarization or to facilitate retrieval from a database. We present automated models that predict the relative importance of different elements in data visualizations and graphic designs. Our models are neural networks trained on human clicks and importance annotations on hundreds of designs. We collected a new dataset of crowdsourced importance, and analyzed the predictions of our models with respect to ground truth importance and human eye movements. We demonstrate how such predictions of importance can be used for automatic design retargeting and thumbnailing. User studies with hundreds of MTurk participants validate that, with limited post-processing, our importance-driven applications are on par with, or outperform, current state-of-the-art methods, including natural image saliency. We also provide a demonstration of how our importance predictions can be built into interactive design tools to offer immediate feedback during the design process.

Learning Visual Importance for Graphic Designs and Data Visualizations

http://arxiv.org/pdf/1708.02660v1

Title Learning Visual Importance Graphic Designs Data Visualizations Summary Knowing people look click visual design provide clue design perceived important relevant content lie important content visual design used effective summarization facilitate retrieval database present automated model predict relative importance different element data visualization graphic design model neural network trained human click importance annotation hundred design collected new dataset crowdsourced importance analyzed prediction model respect ground truth importance human eye movement demonstrate prediction importance used automatic design retargeting thumbnailing User study hundred MTurk participant validate limited postprocessing importancedriven application par outperform current stateoftheart method including natural image saliency also provide demonstration importance prediction built interactive design tool offer immediate feedback design process 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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['S. Kolkur', 'D. Kalbande', 'P. Shimpi', 'C. Bapat', 'J. Jatakia']

1708.02694v1

Human Skin detection deals with the recognition of skin-colored pixels and regions in a given image. Skin color is often used in human skin detection because it is invariant to orientation and size and is fast to process. A new human skin detection algorithm is proposed in this paper. The three main parameters for recognizing a skin pixel are RGB (Red, Green, Blue), HSV (Hue, Saturation, Value) and YCbCr (Luminance, Chrominance) color models. The objective of proposed algorithm is to improve the recognition of skin pixels in given images. The algorithm not only considers individual ranges of the three color parameters but also takes into ac- count combinational ranges which provide greater accuracy in recognizing the skin area in a given image.

Human Skin Detection Using RGB, HSV and YCbCr Color Models

http://arxiv.org/pdf/1708.02694v1

Title Human Skin Detection Using RGB HSV YCbCr Color Models Summary Human Skin detection deal recognition skincolored pixel region given image Skin color often used human skin detection invariant orientation size fast process new human skin detection algorithm proposed paper three main parameter recognizing skin pixel RGB Red Green Blue HSV Hue Saturation Value YCbCr Luminance Chrominance color model objective proposed algorithm improve recognition skin pixel given image algorithm considers individual range three color parameter also take ac count combinational range provide greater accuracy recognizing skin area given 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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['Pedro F. Proença', 'Yang Gao']

1708.02837v1

Active depth cameras suffer from several limitations, which cause incomplete and noisy depth maps, and may consequently affect the performance of RGB-D Odometry. To address this issue, this paper presents a visual odometry method based on point and line features that leverages both measurements from a depth sensor and depth estimates from camera motion. Depth estimates are generated continuously by a probabilistic depth estimation framework for both types of features to compensate for the lack of depth measurements and inaccurate feature depth associations. The framework models explicitly the uncertainty of triangulating depth from both point and line observations to validate and obtain precise estimates. Furthermore, depth measurements are exploited by propagating them through a depth map registration module and using a frame-to-frame motion estimation method that considers 3D-to-2D and 2D-to-3D reprojection errors, independently. Results on RGB-D sequences captured on large indoor and outdoor scenes, where depth sensor limitations are critical, show that the combination of depth measurements and estimates through our approach is able to overcome the absence and inaccuracy of depth measurements.

SPLODE: Semi-Probabilistic Point and Line Odometry with Depth Estimation from RGB-D Camera Motion

http://arxiv.org/pdf/1708.02837v1

Title SPLODE SemiProbabilistic Point Line Odometry Depth Estimation RGBD Camera Motion Summary Active depth camera suffer several limitation cause incomplete noisy depth map may consequently affect performance RGBD Odometry address issue paper present visual odometry method based point line feature leverage measurement depth sensor depth estimate camera motion Depth estimate generated continuously probabilistic depth estimation framework type feature compensate lack depth measurement inaccurate feature depth association framework model explicitly uncertainty triangulating depth point line observation validate obtain precise estimate Furthermore depth measurement exploited propagating depth map registration module using frametoframe motion estimation method considers 3Dto2D 2Dto3D reprojection error independently Results RGBD sequence captured large indoor outdoor scene depth sensor limitation critical show combination depth measurement estimate approach able overcome absence inaccuracy depth 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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['Xin Jin', 'Shiming Ge', 'Chenggen Song']

1708.02872v1

Recently, cloud storage and processing have been widely adopted. Mobile users in one family or one team may automatically backup their photos to the same shared cloud storage space. The powerful face detector trained and provided by a 3rd party may be used to retrieve the photo collection which contains a specific group of persons from the cloud storage server. However, the privacy of the mobile users may be leaked to the cloud server providers. In the meanwhile, the copyright of the face detector should be protected. Thus, in this paper, we propose a protocol of privacy preserving face retrieval in the cloud for mobile users, which protects the user photos and the face detector simultaneously. The cloud server only provides the resources of storage and computing and can not learn anything of the user photos and the face detector. We test our protocol inside several families and classes. The experimental results reveal that our protocol can successfully retrieve the proper photos from the cloud server and protect the user photos and the face detector.

Privacy Preserving Face Retrieval in the Cloud for Mobile Users

http://arxiv.org/pdf/1708.02872v1

Title Privacy Preserving Face Retrieval Cloud Mobile Users Summary Recently cloud storage processing widely adopted Mobile user one family one team may automatically backup photo shared cloud storage space powerful face detector trained provided 3rd party may used retrieve photo collection contains specific group person cloud storage server However privacy mobile user may leaked cloud server provider meanwhile copyright face detector protected Thus paper propose protocol privacy preserving face retrieval cloud mobile user protects user photo face detector simultaneously cloud server provides resource storage computing learn anything user photo face detector test protocol inside several family class experimental result reveal protocol successfully retrieve proper photo cloud server protect user photo face detector 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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['Matthijs Douze', 'Hervé Jégou', 'Jeff Johnson']

1708.02898v1

This paper aims at discovering meaningful subsets of related images from large image collections without annotations. We search groups of images related at different levels of semantic, i.e., either instances or visual classes. While k-means is usually considered as the gold standard for this task, we evaluate and show the interest of diffusion methods that have been neglected by the state of the art, such as the Markov Clustering algorithm. We report results on the ImageNet and the Paris500k instance dataset, both enlarged with images from YFCC100M. We evaluate our methods with a labelling cost that reflects how much effort a human would require to correct the generated clusters. Our analysis highlights several properties. First, when powered with an efficient GPU implementation, the cost of the discovery process is small compared to computing the image descriptors, even for collections as large as 100 million images. Second, we show that descriptions selected for instance search improve the discovery of object classes. Third, the Markov Clustering technique consistently outperforms other methods; to our knowledge it has never been considered in this large scale scenario.

An evaluation of large-scale methods for image instance and class discovery

http://arxiv.org/pdf/1708.02898v1

Title evaluation largescale method image instance class discovery Summary paper aim discovering meaningful subset related image large image collection without annotation search group image related different level semantic ie either instance visual class kmeans usually considered gold standard task evaluate show interest diffusion method neglected state art Markov Clustering algorithm report result ImageNet Paris500k instance dataset enlarged image YFCC100M evaluate method labelling cost reflects much effort human would require correct generated cluster analysis highlight several property First powered efficient GPU implementation cost discovery process small compared computing image descriptor even collection large 100 million image Second show description selected instance search improve discovery object class Third Markov Clustering technique consistently outperforms method knowledge never considered large scale scenario 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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['Tae-Hyun Oh', 'Kyungdon Joo', 'Neel Joshi', 'Baoyuan Wang', 'In So Kweon', 'Sing Bing Kang']

1708.02970v1

Cinemagraphs are a compelling way to convey dynamic aspects of a scene. In these media, dynamic and still elements are juxtaposed to create an artistic and narrative experience. Creating a high-quality, aesthetically pleasing cinemagraph requires isolating objects in a semantically meaningful way and then selecting good start times and looping periods for those objects to minimize visual artifacts (such a tearing). To achieve this, we present a new technique that uses object recognition and semantic segmentation as part of an optimization method to automatically create cinemagraphs from videos that are both visually appealing and semantically meaningful. Given a scene with multiple objects, there are many cinemagraphs one could create. Our method evaluates these multiple candidates and presents the best one, as determined by a model trained to predict human preferences in a collaborative way. We demonstrate the effectiveness of our approach with multiple results and a user study.

Personalized Cinemagraphs using Semantic Understanding and Collaborative Learning

http://arxiv.org/pdf/1708.02970v1

Title Personalized Cinemagraphs using Semantic Understanding Collaborative Learning Summary Cinemagraphs compelling way convey dynamic aspect scene medium dynamic still element juxtaposed create artistic narrative experience Creating highquality aesthetically pleasing cinemagraph requires isolating object semantically meaningful way selecting good start time looping period object minimize visual artifact tearing achieve present new technique us object recognition semantic segmentation part optimization method automatically create cinemagraphs video visually appealing semantically meaningful Given scene multiple object many cinemagraphs one could create method evaluates multiple candidate present best one determined model trained predict human preference collaborative way demonstrate effectiveness approach multiple result user study 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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['Pratul P. Srinivasan', 'Tongzhou Wang', 'Ashwin Sreelal', 'Ravi Ramamoorthi', 'Ren Ng']

1708.03292v1

We present a machine learning algorithm that takes as input a 2D RGB image and synthesizes a 4D RGBD light field (color and depth of the scene in each ray direction). For training, we introduce the largest public light field dataset, consisting of over 3300 plenoptic camera light fields of scenes containing flowers and plants. Our synthesis pipeline consists of a convolutional neural network (CNN) that estimates scene geometry, a stage that renders a Lambertian light field using that geometry, and a second CNN that predicts occluded rays and non-Lambertian effects. Our algorithm builds on recent view synthesis methods, but is unique in predicting RGBD for each light field ray and improving unsupervised single image depth estimation by enforcing consistency of ray depths that should intersect the same scene point. Please see our supplementary video at https://youtu.be/yLCvWoQLnms

Learning to Synthesize a 4D RGBD Light Field from a Single Image

http://arxiv.org/pdf/1708.03292v1

Title Learning Synthesize 4D RGBD Light Field Single Image Summary present machine learning algorithm take input 2D RGB image synthesizes 4D RGBD light field color depth scene ray direction training introduce largest public light field dataset consisting 3300 plenoptic camera light field scene containing flower plant synthesis pipeline consists convolutional neural network CNN estimate scene geometry stage render Lambertian light field using geometry second CNN predicts occluded ray nonLambertian effect algorithm build recent view synthesis method unique predicting RGBD light field ray improving unsupervised single image depth estimation enforcing consistency ray depth intersect scene point Please see supplementary video httpsyoutubeyLCvWoQLnms 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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['Nazim Haouchine', 'Frederick Roy', 'Hadrien Courtecuisse', 'Matthias Nießner', 'Stephane Cotin']

1708.03748v1

We present Calipso, an interactive method for editing images and videos in a physically-coherent manner. Our main idea is to realize physics-based manipulations by running a full physics simulation on proxy geometries given by non-rigidly aligned CAD models. Running these simulations allows us to apply new, unseen forces to move or deform selected objects, change physical parameters such as mass or elasticity, or even add entire new objects that interact with the rest of the underlying scene. In Calipso, the user makes edits directly in 3D; these edits are processed by the simulation and then transfered to the target 2D content using shape-to-image correspondences in a photo-realistic rendering process. To align the CAD models, we introduce an efficient CAD-to-image alignment procedure that jointly minimizes for rigid and non-rigid alignment while preserving the high-level structure of the input shape. Moreover, the user can choose to exploit image flow to estimate scene motion, producing coherent physical behavior with ambient dynamics. We demonstrate Calipso's physics-based editing on a wide range of examples producing myriad physical behavior while preserving geometric and visual consistency.

Calipso: Physics-based Image and Video Editing through CAD Model Proxies

http://arxiv.org/pdf/1708.03748v1

Title Calipso Physicsbased Image Video Editing CAD Model Proxies Summary present Calipso interactive method editing image video physicallycoherent manner main idea realize physicsbased manipulation running full physic simulation proxy geometry given nonrigidly aligned CAD model Running simulation allows u apply new unseen force move deform selected object change physical parameter mass elasticity even add entire new object interact rest underlying scene Calipso user make edits directly 3D edits processed simulation transfered target 2D content using shapetoimage correspondence photorealistic rendering process align CAD model introduce efficient CADtoimage alignment procedure jointly minimizes rigid nonrigid alignment preserving highlevel structure input shape Moreover user choose exploit image flow estimate scene motion producing coherent physical behavior ambient dynamic demonstrate Calipsos physicsbased editing wide range example producing myriad physical behavior preserving geometric visual consistency 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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['Andrey Kurenkov', 'Jingwei Ji', 'Animesh Garg', 'Viraj Mehta', 'JunYoung Gwak', 'Christopher Choy', 'Silvio Savarese']

1708.04672v1

3D reconstruction from a single image is a key problem in multiple applications ranging from robotic manipulation to augmented reality. Prior methods have tackled this problem through generative models which predict 3D reconstructions as voxels or point clouds. However, these methods can be computationally expensive and miss fine details. We introduce a new differentiable layer for 3D data deformation and use it in DeformNet to learn a model for 3D reconstruction-through-deformation. DeformNet takes an image input, searches the nearest shape template from a database, and deforms the template to match the query image. We evaluate our approach on the ShapeNet dataset and show that - (a) the Free-Form Deformation layer is a powerful new building block for Deep Learning models that manipulate 3D data (b) DeformNet uses this FFD layer combined with shape retrieval for smooth and detail-preserving 3D reconstruction of qualitatively plausible point clouds with respect to a single query image (c) compared to other state-of-the-art 3D reconstruction methods, DeformNet quantitatively matches or outperforms their benchmarks by significant margins. For more information, visit: https://deformnet-site.github.io/DeformNet-website/ .

DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image

http://arxiv.org/pdf/1708.04672v1

Title DeformNet FreeForm Deformation Network 3D Shape Reconstruction Single Image Summary 3D reconstruction single image key problem multiple application ranging robotic manipulation augmented reality Prior method tackled problem generative model predict 3D reconstruction voxels point cloud However method computationally expensive miss fine detail introduce new differentiable layer 3D data deformation use DeformNet learn model 3D reconstructionthroughdeformation DeformNet take image input search nearest shape template database deforms template match query image evaluate approach ShapeNet dataset show FreeForm Deformation layer powerful new building block Deep Learning model manipulate 3D data b DeformNet us FFD layer combined shape retrieval smooth detailpreserving 3D reconstruction qualitatively plausible point cloud respect single query image c compared stateoftheart 3D reconstruction method DeformNet quantitatively match outperforms benchmark significant margin information visit httpsdeformnetsitegithubioDeformNetwebsite 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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['Bariscan Yonel', 'Eric Mason', 'Birsen Yazıcı']

1708.04682v1

We introduce a deep learning (DL) framework for inverse problems in imaging, and demonstrate the advantages and applicability of this approach in passive synthetic aperture radar (SAR) image reconstruction. We interpret image recon- struction as a machine learning task and utilize deep networks as forward and inverse solvers for imaging. Specifically, we design a recurrent neural network (RNN) architecture as an inverse solver based on the iterations of proximal gradient descent optimization methods. We further adapt the RNN architecture to image reconstruction problems by transforming the network into a recurrent auto-encoder, thereby allowing for unsupervised training. Our DL based inverse solver is particularly suitable for a class of image formation problems in which the forward model is only partially known. The ability to learn forward models and hyper parameters combined with unsupervised training approach establish our recurrent auto-encoder suitable for real world applications. We demonstrate the performance of our method in passive SAR image reconstruction. In this regime a source of opportunity, with unknown location and transmitted waveform, is used to illuminate a scene of interest. We investigate recurrent auto- encoder architecture based on the 1 and 0 constrained least- squares problem. We present a projected stochastic gradient descent based training scheme which incorporates constraints of the unknown model parameters. We demonstrate through extensive numerical simulations that our DL based approach out performs conventional sparse coding methods in terms of computation and reconstructed image quality, specifically, when no information about the transmitter is available.

Deep Learning for Passive Synthetic Aperture Radar

http://arxiv.org/pdf/1708.04682v1

Title Deep Learning Passive Synthetic Aperture Radar Summary introduce deep learning DL framework inverse problem imaging demonstrate advantage applicability approach passive synthetic aperture radar SAR image reconstruction interpret image recon struction machine learning task utilize deep network forward inverse solver imaging Specifically design recurrent neural network RNN architecture inverse solver based iteration proximal gradient descent optimization method adapt RNN architecture image reconstruction problem transforming network recurrent autoencoder thereby allowing unsupervised training DL based inverse solver particularly suitable class image formation problem forward model partially known ability learn forward model hyper parameter combined unsupervised training approach establish recurrent autoencoder suitable real world application demonstrate performance method passive SAR image reconstruction regime source opportunity unknown location transmitted waveform used illuminate scene interest investigate recurrent auto encoder architecture based 1 0 constrained least square problem present projected stochastic gradient descent based training scheme incorporates constraint unknown model parameter demonstrate extensive numerical simulation DL based approach performs conventional sparse coding method term computation reconstructed image quality specifically information transmitter available 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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['Renata Rychtarikova', 'Pavel Soucek', 'Dalibor Stys']

1708.04685v1

In this paper, we introduce a novel - physico-chemical - approach for calibration of a digital camera chip. This approach utilizes results of measurement of incident light spectra of calibration films of different levels of gray for construction of calibration curve (number of incident photons vs. image pixel intensity) for each camera pixel. We show spectral characteristics of such corrected digital raw image files (a primary camera signal) and demonstrate their suitability for next image processing and analysis.

Colorimetric Calibration of a Digital Camera

http://arxiv.org/pdf/1708.04685v1

Title Colorimetric Calibration Digital Camera Summary paper introduce novel physicochemical approach calibration digital camera chip approach utilizes result measurement incident light spectrum calibration film different level gray construction calibration curve number incident photon v image pixel intensity camera pixel show spectral characteristic corrected digital raw image file primary camera signal demonstrate suitability next image processing analysis 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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['Nikolaos Mitianoudis']

1708.04816v1

Directional or Circular statistics are pertaining to the analysis and interpretation of directions or rotations. In this work, a novel probability distribution is proposed to model multidimensional sparse directional data. The Generalised Directional Laplacian Distribution (DLD) is a hybrid between the Laplacian distribution and the von Mises-Fisher distribution. The distribution's parameters are estimated using Maximum-Likelihood Estimation over a set of training data points. Mixtures of Directional Laplacian Distributions (MDLD) are also introduced in order to model multiple concentrations of sparse directional data. The author explores the application of the derived DLD mixture model to cluster sound sources that exist in an underdetermined instantaneous sound mixture. The proposed model can solve the general K x L (K<L) underdetermined instantaneous source separation problem, offering a fast and stable solution.

A Generalised Directional Laplacian Distribution: Estimation, Mixture Models and Audio Source Separation

http://arxiv.org/pdf/1708.04816v1

Title Generalised Directional Laplacian Distribution Estimation Mixture Models Audio Source Separation Summary Directional Circular statistic pertaining analysis interpretation direction rotation work novel probability distribution proposed model multidimensional sparse directional data Generalised Directional Laplacian Distribution DLD hybrid Laplacian distribution von MisesFisher distribution distribution parameter estimated using MaximumLikelihood Estimation set training data point Mixtures Directional Laplacian Distributions MDLD also introduced order model multiple concentration sparse directional data author explores application derived DLD mixture model cluster sound source exist underdetermined instantaneous sound mixture proposed model solve general K x L KL underdetermined instantaneous source separation problem offering fast stable solution Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Elizabeth Lucas']

1708.04879v1

Interstitial content is online content which grays out, or otherwise obscures the main page content. In this technical report, we discuss exploratory research into detecting the presence of interstitial content in web pages. We discuss the use of computer vision techniques to detect interstitials, and the potential use of these techniques to provide a labelled dataset for machine learning.

Interstitial Content Detection

http://arxiv.org/pdf/1708.04879v1

Title Interstitial Content Detection Summary Interstitial content online content gray otherwise obscures main page content technical report discus exploratory research detecting presence interstitial content web page discus use computer vision technique detect interstitials potential use technique provide labelled dataset machine learning 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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['Xuelong Li', 'Di Hu', 'Feiping Nie']

1708.05127v2

With the increasing demand of massive multimodal data storage and organization, cross-modal retrieval based on hashing technique has drawn much attention nowadays. It takes the binary codes of one modality as the query to retrieve the relevant hashing codes of another modality. However, the existing binary constraint makes it difficult to find the optimal cross-modal hashing function. Most approaches choose to relax the constraint and perform thresholding strategy on the real-value representation instead of directly solving the original objective. In this paper, we first provide a concrete analysis about the effectiveness of multimodal networks in preserving the inter- and intra-modal consistency. Based on the analysis, we provide a so-called Deep Binary Reconstruction (DBRC) network that can directly learn the binary hashing codes in an unsupervised fashion. The superiority comes from a proposed simple but efficient activation function, named as Adaptive Tanh (ATanh). The ATanh function can adaptively learn the binary codes and be trained via back-propagation. Extensive experiments on three benchmark datasets demonstrate that DBRC outperforms several state-of-the-art methods in both image2text and text2image retrieval task.

Deep Binary Reconstruction for Cross-modal Hashing

http://arxiv.org/pdf/1708.05127v2

Title Deep Binary Reconstruction Crossmodal Hashing Summary increasing demand massive multimodal data storage organization crossmodal retrieval based hashing technique drawn much attention nowadays take binary code one modality query retrieve relevant hashing code another modality However existing binary constraint make difficult find optimal crossmodal hashing function approach choose relax constraint perform thresholding strategy realvalue representation instead directly solving original objective paper first provide concrete analysis effectiveness multimodal network preserving inter intramodal consistency Based analysis provide socalled Deep Binary Reconstruction DBRC network directly learn binary hashing code unsupervised fashion superiority come proposed simple efficient activation function named Adaptive Tanh ATanh ATanh function adaptively learn binary code trained via backpropagation Extensive experiment three benchmark datasets demonstrate DBRC outperforms several stateoftheart method image2text text2image retrieval 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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['Mina Rezaei', 'Haojin Yang', 'Christoph Meinel']

1708.05206v1

In this paper, we describe our method for classification of brain magnetic resonance (MR) images into different abnormalities and healthy classes based on the deep neural network. We propose our method to detect high and low-grade glioma, multiple sclerosis, and Alzheimer diseases as well as healthy cases. Our network architecture has ten learning layers that include seven convolutional layers and three fully connected layers. We have achieved a promising result in five categories of brain images (classification task) with 95.7% accuracy.

Brain Abnormality Detection by Deep Convolutional Neural Network

http://arxiv.org/pdf/1708.05206v1

Title Brain Abnormality Detection Deep Convolutional Neural Network Summary paper describe method classification brain magnetic resonance MR image different abnormality healthy class based deep neural network propose method detect high lowgrade glioma multiple sclerosis Alzheimer disease well healthy case network architecture ten learning layer include seven convolutional layer three fully connected layer achieved promising result five category brain image classification task 957 accuracy 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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['Weimin Wang', 'Ken Sakurada', 'Nobuo Kawaguchi']

1708.05514v1

This paper presents a novel method for fully automatic and convenient extrinsic calibration of a 3D LiDAR and a panoramic camera with a normally printed chessboard. The proposed method is based on the 3D corner estimation of the chessboard from the sparse point cloud generated by one frame scan of the LiDAR. To estimate the corners, we formulate a full-scale model of the chessboard and fit it to the segmented 3D points of the chessboard. The model is fitted by optimizing the cost function under constraints of correlation between the reflectance intensity of laser and the color of the chessboard's patterns. Powell's method is introduced for resolving the discontinuity problem in optimization. The corners of the fitted model are considered as the 3D corners of the chessboard. Once the corners of the chessboard in the 3D point cloud are estimated, the extrinsic calibration of the two sensors is converted to a 3D-2D matching problem. The corresponding 3D-2D points are used to calculate the absolute pose of the two sensors with Unified Perspective-n-Point (UPnP). Further, the calculated parameters are regarded as initial values and are refined using the Levenberg-Marquardt method. The performance of the proposed corner detection method from the 3D point cloud is evaluated using simulations. The results of experiments, conducted on a Velodyne HDL-32e LiDAR and a Ladybug3 camera under the proposed re-projection error metric, qualitatively and quantitatively demonstrate the accuracy and stability of the final extrinsic calibration parameters.

Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard

http://arxiv.org/pdf/1708.05514v1

Title Reflectance Intensity Assisted Automatic Accurate Extrinsic Calibration 3D LiDAR Panoramic Camera Using Printed Chessboard Summary paper present novel method fully automatic convenient extrinsic calibration 3D LiDAR panoramic camera normally printed chessboard proposed method based 3D corner estimation chessboard sparse point cloud generated one frame scan LiDAR estimate corner formulate fullscale model chessboard fit segmented 3D point chessboard model fitted optimizing cost function constraint correlation reflectance intensity laser color chessboard pattern Powells method introduced resolving discontinuity problem optimization corner fitted model considered 3D corner chessboard corner chessboard 3D point cloud estimated extrinsic calibration two sensor converted 3D2D matching problem corresponding 3D2D point used calculate absolute pose two sensor Unified PerspectivenPoint UPnP calculated parameter regarded initial value refined using LevenbergMarquardt method performance proposed corner detection method 3D point cloud evaluated using simulation result experiment conducted Velodyne HDL32e LiDAR Ladybug3 camera proposed reprojection error metric qualitatively quantitatively demonstrate accuracy stability final extrinsic calibration parameter 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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['Partha Pratim Roy', 'Ayan Kumar Bhunia', 'Avirup Bhattacharyya', 'Umapada Pal']

1708.05529v2

Retrieval of text information from natural scene images and video frames is a challenging task due to its inherent problems like complex character shapes, low resolution, background noise, etc. Available OCR systems often fail to retrieve such information in scene/video frames. Keyword spotting, an alternative way to retrieve information, performs efficient text searching in such scenarios. However, current word spotting techniques in scene/video images are script-specific and they are mainly developed for Latin script. This paper presents a novel word spotting framework using dynamic shape coding for text retrieval in natural scene image and video frames. The framework is designed to search query keyword from multiple scripts with the help of on-the-fly script-wise keyword generation for the corresponding script. We have used a two-stage word spotting approach using Hidden Markov Model (HMM) to detect the translated keyword in a given text line by identifying the script of the line. A novel unsupervised dynamic shape coding based scheme has been used to group similar shape characters to avoid confusion and to improve text alignment. Next, the hypotheses locations are verified to improve retrieval performance. To evaluate the proposed system for searching keyword from natural scene image and video frames, we have considered two popular Indic scripts such as Bangla (Bengali) and Devanagari along with English. Inspired by the zone-wise recognition approach in Indic scripts[1], zone-wise text information has been used to improve the traditional word spotting performance in Indic scripts. For our experiment, a dataset consisting of images of different scenes and video frames of English, Bangla and Devanagari scripts were considered. The results obtained showed the effectiveness of our proposed word spotting approach.

Word Searching in Scene Image and Video Frame in Multi-Script Scenario using Dynamic Shape Coding

http://arxiv.org/pdf/1708.05529v2

Title Word Searching Scene Image Video Frame MultiScript Scenario using Dynamic Shape Coding Summary Retrieval text information natural scene image video frame challenging task due inherent problem like complex character shape low resolution background noise etc Available OCR system often fail retrieve information scenevideo frame Keyword spotting alternative way retrieve information performs efficient text searching scenario However current word spotting technique scenevideo image scriptspecific mainly developed Latin script paper present novel word spotting framework using dynamic shape coding text retrieval natural scene image video frame framework designed search query keyword multiple script help onthefly scriptwise keyword generation corresponding script used twostage word spotting approach using Hidden Markov Model HMM detect translated keyword given text line identifying script line novel unsupervised dynamic shape coding based scheme used group similar shape character avoid confusion improve text alignment Next hypothesis location verified improve retrieval performance evaluate proposed system searching keyword natural scene image video frame considered two popular Indic script Bangla Bengali Devanagari along English Inspired zonewise recognition approach Indic scripts1 zonewise text information used improve traditional word spotting performance Indic script experiment dataset consisting image different scene video frame English Bangla Devanagari script considered result obtained showed effectiveness proposed word spotting 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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['Yazhou Yao', 'Jian Zhang', 'Fumin Shen', 'Dongxiang Zhang', 'Zhenmin Tang', 'Heng Tao Shen']

1708.06495v1

The availability of labeled image datasets has been shown critical for high-level image understanding, which continuously drives the progress of feature designing and models developing. However, constructing labeled image datasets is laborious and monotonous. To eliminate manual annotation, in this work, we propose a novel image dataset construction framework by employing multiple textual metadata. We aim at collecting diverse and accurate images for given queries from the Web. Specifically, we formulate noisy textual metadata removing and noisy images filtering as a multi-view and multi-instance learning problem separately. Our proposed approach not only improves the accuracy but also enhances the diversity of the selected images. To verify the effectiveness of our proposed approach, we construct an image dataset with 100 categories. The experiments show significant performance gains by using the generated data of our approach on several tasks, such as image classification, cross-dataset generalization, and object detection. The proposed method also consistently outperforms existing weakly supervised and web-supervised approaches.

Towards Automatic Construction of Diverse, High-quality Image Dataset

http://arxiv.org/pdf/1708.06495v1

Title Towards Automatic Construction Diverse Highquality Image Dataset Summary availability labeled image datasets shown critical highlevel image understanding continuously drive progress feature designing model developing However constructing labeled image datasets laborious monotonous eliminate manual annotation work propose novel image dataset construction framework employing multiple textual metadata aim collecting diverse accurate image given query Web Specifically formulate noisy textual metadata removing noisy image filtering multiview multiinstance learning problem separately proposed approach improves accuracy also enhances diversity selected image verify effectiveness proposed approach construct image dataset 100 category experiment show significant performance gain using generated data approach several task image classification crossdataset generalization object detection proposed method also consistently outperforms existing weakly supervised websupervised 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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['Fernando Wario', 'Benjamin Wild', 'Raúl Rojas', 'Tim Landgraf']

1708.06590v3

The waggle dance is one of the most popular examples of animal communication. Forager bees direct their nestmates to profitable resources via a complex motor display. Essentially, the dance encodes the polar coordinates to the resource in the field. Unemployed foragers follow the dancer's movements and then search for the advertised spots in the field. Throughout the last decades, biologists have employed different techniques to measure key characteristics of the waggle dance and decode the information it conveys. Early techniques involved the use of protractors and stopwatches to measure the dance orientation and duration directly from the observation hive. Recent approaches employ digital video recordings and manual measurements on screen. However, manual approaches are very time-consuming. Most studies, therefore, regard only small numbers of animals in short periods of time. We have developed a system capable of automatically detecting, decoding and mapping communication dances in real-time. In this paper, we describe our recording setup, the image processing steps performed for dance detection and decoding and an algorithm to map dances to the field. The proposed system performs with a detection accuracy of 90.07\%. The decoded waggle orientation has an average error of -2.92{\deg} ($\pm$ 7.37{\deg} ), well within the range of human error. To evaluate and exemplify the system's performance, a group of bees was trained to an artificial feeder, and all dances in the colony were automatically detected, decoded and mapped. The system presented here is the first of this kind made publicly available, including source code and hardware specifications. We hope this will foster quantitative analyses of the honey bee waggle dance.

Automatic detection and decoding of honey bee waggle dances

http://arxiv.org/pdf/1708.06590v3

Title Automatic detection decoding honey bee waggle dance Summary waggle dance one popular example animal communication Forager bee direct nestmates profitable resource via complex motor display Essentially dance encodes polar coordinate resource field Unemployed forager follow dancer movement search advertised spot field Throughout last decade biologist employed different technique measure key characteristic waggle dance decode information conveys Early technique involved use protractor stopwatch measure dance orientation duration directly observation hive Recent approach employ digital video recording manual measurement screen However manual approach timeconsuming study therefore regard small number animal short period time developed system capable automatically detecting decoding mapping communication dance realtime paper describe recording setup image processing step performed dance detection decoding algorithm map dance field proposed system performs detection accuracy 9007 decoded waggle orientation average error 292deg pm 737deg well within range human error evaluate exemplify system performance group bee trained artificial feeder dance colony automatically detected decoded mapped system presented first kind made publicly available including source code hardware specification hope foster quantitative analysis honey bee waggle dance 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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['Sanjeev Muralikrishnan', 'Vladimir G. Kim', 'Siddhartha Chaudhuri']

1708.06673v2

We propose a novel method for discovering shape regions that strongly correlate with user-prescribed tags. For example, given a collection of chairs tagged as either "has armrest" or "lacks armrest", our system correctly highlights the armrest regions as the main distinctive parts between the two chair types. To obtain point-wise predictions from shape-wise tags we develop a novel neural network architecture that is trained with tag classification loss, but is designed to rely on segmentation to predict the tag. Our network is inspired by U-Net, but we replicate shallow U structures several times with new skip connections and pooling layers, and call the resulting architecture "WU-Net". We test our method on segmentation benchmarks and show that even with weak supervision of whole shape tags, our method can infer meaningful semantic regions, without ever observing shape segmentations. Further, once trained, the model can process shapes for which the tag is entirely unknown. As a bonus, our architecture is directly operational under full supervision and performs strongly on standard benchmarks. We validate our method through experiments with many variant architectures and prior baselines, and demonstrate several applications.

Tags2Parts: Discovering Semantic Regions from Shape Tags

http://arxiv.org/pdf/1708.06673v2

Title Tags2Parts Discovering Semantic Regions Shape Tags Summary propose novel method discovering shape region strongly correlate userprescribed tag example given collection chair tagged either armrest lack armrest system correctly highlight armrest region main distinctive part two chair type obtain pointwise prediction shapewise tag develop novel neural network architecture trained tag classification loss designed rely segmentation predict tag network inspired UNet replicate shallow U structure several time new skip connection pooling layer call resulting architecture WUNet test method segmentation benchmark show even weak supervision whole shape tag method infer meaningful semantic region without ever observing shape segmentation trained model process shape tag entirely unknown bonus architecture directly operational full supervision performs strongly standard benchmark validate method experiment many variant architecture prior baseline demonstrate several 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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['Aviv Gabbay', 'Ariel Ephrat', 'Tavi Halperin', 'Shmuel Peleg']

1708.06767v3

Isolating the voice of a specific person while filtering out other voices or background noises is challenging when video is shot in noisy environments. We propose audio-visual methods to isolate the voice of a single speaker and eliminate unrelated sounds. First, face motions captured in the video are used to estimate the speaker's voice, by passing the silent video frames through a video-to-speech neural network-based model. Then the speech predictions are applied as a filter on the noisy input audio. This approach avoids using mixtures of sounds in the learning process, as the number of such possible mixtures is huge, and would inevitably bias the trained model. We evaluate our method on two audio-visual datasets, GRID and TCD-TIMIT, and show that our method attains significant SDR and PESQ improvements over the raw video-to-speech predictions, and a well-known audio-only method.

Seeing Through Noise: Visually Driven Speaker Separation and Enhancement

http://arxiv.org/pdf/1708.06767v3

Title Seeing Noise Visually Driven Speaker Separation Enhancement Summary Isolating voice specific person filtering voice background noise challenging video shot noisy environment propose audiovisual method isolate voice single speaker eliminate unrelated sound First face motion captured video used estimate speaker voice passing silent video frame videotospeech neural networkbased model speech prediction applied filter noisy input audio approach avoids using mixture sound learning process number possible mixture huge would inevitably bias trained model evaluate method two audiovisual datasets GRID TCDTIMIT show method attains significant SDR PESQ improvement raw videotospeech prediction wellknown audioonly 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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['Celia Fernández Madrazo', 'Ignacio Heredia Cacha', 'Lara Lloret Iglesias', 'Jesús Marco de Lucas']

1708.07034v1

The application of deep learning techniques using convolutional neural networks to the classification of particle collisions in High Energy Physics is explored. An intuitive approach to transform physical variables, like momenta of particles and jets, into a single image that captures the relevant information, is proposed. The idea is tested using a well known deep learning framework on a simulation dataset, including leptonic ttbar events and the corresponding background at 7 TeV from the CMS experiment at LHC, available as Open Data. This initial test shows competitive results when compared to more classical approaches, like those using feedforward neural networks.

Application of a Convolutional Neural Network for image classification to the analysis of collisions in High Energy Physics

http://arxiv.org/pdf/1708.07034v1

Title Application Convolutional Neural Network image classification analysis collision High Energy Physics Summary application deep learning technique using convolutional neural network classification particle collision High Energy Physics explored intuitive approach transform physical variable like momentum particle jet single image capture relevant information proposed idea tested using well known deep learning framework simulation dataset including leptonic ttbar event corresponding background 7 TeV CMS experiment LHC available Open Data initial test show competitive result compared classical approach like using feedforward neural 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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['Inwook Shim', 'Tae-Hyun Oh', 'Joon-Young Lee', 'Dong-Geol Choi', 'Jinwook Choi', 'In So Kweon']

1708.07338v2

We introduce a novel method to automatically adjust camera exposure for image processing and computer vision applications of mobile robot platforms. Since most image processing algorithms heavily rely on low-level image features, which are largely based on local gradient information, we consider a gradient quantity to determine a proper exposure level, so that a camera is able to capture important image features robust to illumination conditions. We extend it to multi-camera system and present a new control algorithm to achieve both brightness consistency between adjacent cameras and a proper exposure level for each camera. We implement our prototype system with off-the-shelf machine vision cameras and demonstrate the effectiveness of the proposed algorithms on practical applications: pedestrian detection, visual odometry, surround-view imaging, panoramic imaging, and stereo matching.

Gradient-based Camera Exposure Control for Outdoor Mobile Platforms

http://arxiv.org/pdf/1708.07338v2

Title Gradientbased Camera Exposure Control Outdoor Mobile Platforms Summary introduce novel method automatically adjust camera exposure image processing computer vision application mobile robot platform Since image processing algorithm heavily rely lowlevel image feature largely based local gradient information consider gradient quantity determine proper exposure level camera able capture important image feature robust illumination condition extend multicamera system present new control algorithm achieve brightness consistency adjacent camera proper exposure level camera implement prototype system offtheshelf machine vision camera demonstrate effectiveness proposed algorithm practical application pedestrian detection visual odometry surroundview imaging panoramic imaging stereo matching Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ahmed F. Hussein', 'Abbas K. AlZubaidi', 'Ali Al-Bayaty', 'Qais A. Habash']

1708.08189v1

The conventional authentication technologies, like RFID tags and authentication cards/badges, suffer from different weaknesses, therefore a prompt replacement to use biometric method of authentication should be applied instead. Biometrics, such as fingerprints, voices, and ECG signals, are unique human characters that can be used for authentication processing. In this work, we present an IoT real-time authentication system based on using extracted ECG features to identify the unknown persons. The Discrete Cosine Transform (DCT) is used as an ECG feature extraction, where it has better characteristics for real-time system implementations. There are a substantial number of researches with a high accuracy of authentication, but most of them ignore the real-time capability of authenticating individuals. With the accuracy rate of 97.78% at around 1.21 seconds of processing time, the proposed system is more suitable for use in many applications that require fast and reliable authentication processing demands.

An IoT Real-Time Biometric Authentication System Based on ECG Fiducial Extracted Features Using Discrete Cosine Transform

http://arxiv.org/pdf/1708.08189v1

Title IoT RealTime Biometric Authentication System Based ECG Fiducial Extracted Features Using Discrete Cosine Transform Summary conventional authentication technology like RFID tag authentication cardsbadges suffer different weakness therefore prompt replacement use biometric method authentication applied instead Biometrics fingerprint voice ECG signal unique human character used authentication processing work present IoT realtime authentication system based using extracted ECG feature identify unknown person Discrete Cosine Transform DCT used ECG feature extraction better characteristic realtime system implementation substantial number research high accuracy authentication ignore realtime capability authenticating individual accuracy rate 9778 around 121 second processing time proposed system suitable use many application require fast reliable authentication processing demand 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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['Tianyue Zheng', 'Weihong Deng', 'Jiani Hu']

1708.08197v1

Labeled Faces in the Wild (LFW) database has been widely utilized as the benchmark of unconstrained face verification and due to big data driven machine learning methods, the performance on the database approaches nearly 100%. However, we argue that this accuracy may be too optimistic because of some limiting factors. Besides different poses, illuminations, occlusions and expressions, cross-age face is another challenge in face recognition. Different ages of the same person result in large intra-class variations and aging process is unavoidable in real world face verification. However, LFW does not pay much attention on it. Thereby we construct a Cross-Age LFW (CALFW) which deliberately searches and selects 3,000 positive face pairs with age gaps to add aging process intra-class variance. Negative pairs with same gender and race are also selected to reduce the influence of attribute difference between positive/negative pairs and achieve face verification instead of attributes classification. We evaluate several metric learning and deep learning methods on the new database. Compared to the accuracy on LFW, the accuracy drops about 10%-17% on CALFW.

Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments

http://arxiv.org/pdf/1708.08197v1

Title CrossAge LFW Database Studying CrossAge Face Recognition Unconstrained Environments Summary Labeled Faces Wild LFW database widely utilized benchmark unconstrained face verification due big data driven machine learning method performance database approach nearly 100 However argue accuracy may optimistic limiting factor Besides different pose illumination occlusion expression crossage face another challenge face recognition Different age person result large intraclass variation aging process unavoidable real world face verification However LFW pay much attention Thereby construct CrossAge LFW CALFW deliberately search selects 3000 positive face pair age gap add aging process intraclass variance Negative pair gender race also selected reduce influence attribute difference positivenegative pair achieve face verification instead attribute classification evaluate several metric learning deep learning method new database Compared accuracy LFW accuracy drop 1017 CALFW 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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['Ruxin Wang', 'Wei Lu', 'Shijun Xiang', 'Xianfeng Zhao', 'Jinwei Wang']

1708.08245v3

Image splicing detection is of fundamental importance in digital forensics and therefore has attracted increasing attention recently. In this paper, a color image splicing detection approach is proposed based on Markov transition probability of quaternion component separation in quaternion discrete cosine transform (QDCT) domain and quaternion wavelet transform (QWT) domain. Firstly, Markov features of the intra-block and inter-block between block QDCT coefficients are obtained from the real part and three imaginary parts of QDCT coefficients respectively. Then, additional Markov features are extracted from luminance (Y) channel in quaternion wavelet transform domain to characterize the dependency of position among quaternion wavelet subband coefficients. Finally, ensemble classifier (EC) is exploited to classify the spliced and authentic color images. The experiment results demonstrate that the proposed approach can outperforms some state-of-the-art methods.

Digital image splicing detection based on Markov features in QDCT and QWT domain

http://arxiv.org/pdf/1708.08245v3

Title Digital image splicing detection based Markov feature QDCT QWT domain Summary Image splicing detection fundamental importance digital forensics therefore attracted increasing attention recently paper color image splicing detection approach proposed based Markov transition probability quaternion component separation quaternion discrete cosine transform QDCT domain quaternion wavelet transform QWT domain Firstly Markov feature intrablock interblock block QDCT coefficient obtained real part three imaginary part QDCT coefficient respectively additional Markov feature extracted luminance channel quaternion wavelet transform domain characterize dependency position among quaternion wavelet subband coefficient Finally ensemble classifier EC exploited classify spliced authentic color image experiment result demonstrate proposed approach outperforms stateoftheart 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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['Soumyabrata Dev', 'Shilpa Manandhar', 'Yee Hui Lee', 'Stefan Winkler']

1708.08760v1

The estimation of total solar irradiance falling on the earth's surface is important in the field of solar energy generation and forecasting. Several clear-sky solar radiation models have been developed over the last few decades. Most of these models are based on empirical distribution of various geographical parameters; while a few models consider various atmospheric effects in the solar energy estimation. In this paper, we perform a comparative analysis of several popular clear-sky models, in the tropical region of Singapore. This is important in countries like Singapore, where we are primarily focused on reliable and efficient solar energy generation. We analyze and compare three popular clear-sky models that are widely used in the literature. We validate our solar estimation results using actual solar irradiance measurements obtained from collocated weather stations. We finally conclude the most reliable clear sky model for Singapore, based on all clear sky days in a year.

Study of Clear Sky Models for Singapore

http://arxiv.org/pdf/1708.08760v1

Title Study Clear Sky Models Singapore Summary estimation total solar irradiance falling earth surface important field solar energy generation forecasting Several clearsky solar radiation model developed last decade model based empirical distribution various geographical parameter model consider various atmospheric effect solar energy estimation paper perform comparative analysis several popular clearsky model tropical region Singapore important country like Singapore primarily focused reliable efficient solar energy generation analyze compare three popular clearsky model widely used literature validate solar estimation result using actual solar irradiance measurement obtained collocated weather station finally conclude reliable clear sky model Singapore based clear sky day year 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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['Tuan Ho', 'Madhukar Budagavi']

1708.08988v1

Dual-fisheye lens cameras have been increasingly used for 360-degree immersive imaging. However, the limited overlapping field of views and misalignment between the two lenses give rise to visible discontinuities in the stitching boundaries. This paper introduces a novel method for dual-fisheye camera stitching that adaptively minimizes the discontinuities in the overlapping regions to generate full spherical 360-degree images. Results show that this approach can produce good quality stitched images for Samsung Gear 360 -- a dual-fisheye camera, even with hard-to-stitch objects in the stitching borders.

Dual-fisheye lens stitching for 360-degree imaging

http://arxiv.org/pdf/1708.08988v1

Title Dualfisheye lens stitching 360degree imaging Summary Dualfisheye lens camera increasingly used 360degree immersive imaging However limited overlapping field view misalignment two lens give rise visible discontinuity stitching boundary paper introduces novel method dualfisheye camera stitching adaptively minimizes discontinuity overlapping region generate full spherical 360degree image Results show approach produce good quality stitched image Samsung Gear 360 dualfisheye camera even hardtostitch object stitching border 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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['Shilpa Manandhar', 'Soumyabrata Dev', 'Yee Hui Lee', 'Yu Song Meng']

1708.08995v1

Clouds play a significant role in the fluctuation of solar radiation received by the earth's surface. It is important to study the various cloud properties, as it impacts the total solar irradiance falling on the earth's surface. One of such important optical properties of the cloud is the Cloud Optical Thickness (COT). It is defined with the amount of light that can pass through the clouds. The COT values are generally obtained from satellite images. However, satellite images have a low temporal- and spatial- resolutions; and are not suitable for study in applications as solar energy generation and forecasting. Therefore, ground-based sky cameras are now getting popular in such fields. In this paper, we analyze the cloud optical thickness value, from the ground-based sky cameras, and provide future research directions.

Analyzing Cloud Optical Properties Using Sky Cameras

http://arxiv.org/pdf/1708.08995v1

Title Analyzing Cloud Optical Properties Using Sky Cameras Summary Clouds play significant role fluctuation solar radiation received earth surface important study various cloud property impact total solar irradiance falling earth surface One important optical property cloud Cloud Optical Thickness COT defined amount light pas cloud COT value generally obtained satellite image However satellite image low temporal spatial resolution suitable study application solar energy generation forecasting Therefore groundbased sky camera getting popular field paper analyze cloud optical thickness value groundbased sky camera provide future research 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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['Mauro Zucchelli', 'Maxime Descoteaux', 'Gloria Menegaz']

1708.08999v2

Diffusion Magnetic Resonance Imaging (DMRI) is the only non-invasive imaging technique which is able to detect the principal directions of water diffusion as well as neurites density in the human brain. Exploiting the ability of Spherical Harmonics (SH) to model spherical functions, we propose a new reconstruction model for DMRI data which is able to estimate both the fiber Orientation Distribution Function (fODF) and the relative volume fractions of the neurites in each voxel, which is robust to multiple fiber crossings. We consider a Neurite Orientation Dispersion and Density Imaging (NODDI) inspired single fiber diffusion signal to be derived from three compartments: intracellular, extracellular, and cerebrospinal fluid. The model, called NODDI-SH, is derived by convolving the single fiber response with the fODF in each voxel. NODDI-SH embeds the calculation of the fODF and the neurite density in a unified mathematical model providing efficient, robust and accurate results. Results were validated on simulated data and tested on \textit{in-vivo} data of human brain, and compared to and Constrained Spherical Deconvolution (CSD) for benchmarking. Results revealed competitive performance in all respects and inherent adaptivity to local microstructure, while sensibly reducing the computational cost. We also investigated NODDI-SH performance when only a limited number of samples are available for the fitting, demonstrating that 60 samples are enough to obtain reliable results. The fast computational time and the low number of signal samples required, make NODDI-SH feasible for clinical application.

NODDI-SH: a computational efficient NODDI extension for fODF estimation in diffusion MRI

http://arxiv.org/pdf/1708.08999v2

Title NODDISH computational efficient NODDI extension fODF estimation diffusion MRI Summary Diffusion Magnetic Resonance Imaging DMRI noninvasive imaging technique able detect principal direction water diffusion well neurites density human brain Exploiting ability Spherical Harmonics SH model spherical function propose new reconstruction model DMRI data able estimate fiber Orientation Distribution Function fODF relative volume fraction neurites voxel robust multiple fiber crossing consider Neurite Orientation Dispersion Density Imaging NODDI inspired single fiber diffusion signal derived three compartment intracellular extracellular cerebrospinal fluid model called NODDISH derived convolving single fiber response fODF voxel NODDISH embeds calculation fODF neurite density unified mathematical model providing efficient robust accurate result Results validated simulated data tested textitinvivo data human brain compared Constrained Spherical Deconvolution CSD benchmarking Results revealed competitive performance respect inherent adaptivity local microstructure sensibly reducing computational cost also investigated NODDISH performance limited number sample available fitting demonstrating 60 sample enough obtain reliable result fast computational time low number signal sample required make NODDISH feasible clinical 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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['Anh Nguyen', 'Thanh-Toan Do', 'Darwin G. Caldwell', 'Nikos G. Tsagarakis']

1708.09011v2

We present a new method to relocalize the 6DOF pose of an event camera solely based on the event stream. Our method first creates the event image from a list of events that occurs in a very short time interval, then a Stacked Spatial LSTM Network (SP-LSTM) is used to learn the camera pose. Our SP-LSTM is composed of a CNN to learn deep features from the event images and a stack of LSTM to learn spatial dependencies in the image feature space. We show that the spatial dependency plays an important role in the relocalization task and the SP-LSTM can effectively learn this information. The experimental results on a publicly available dataset show that our approach generalizes well and outperforms recent methods by a substantial margin. Overall, our proposed method reduces by approx. 6 times the position error and 3 times the orientation error compared to the current state of the art. The source code and trained models will be released.

Real-Time 6DOF Pose Relocalization for Event Cameras with Stacked Spatial LSTM Networks

http://arxiv.org/pdf/1708.09011v2

Title RealTime 6DOF Pose Relocalization Event Cameras Stacked Spatial LSTM Networks Summary present new method relocalize 6DOF pose event camera solely based event stream method first creates event image list event occurs short time interval Stacked Spatial LSTM Network SPLSTM used learn camera pose SPLSTM composed CNN learn deep feature event image stack LSTM learn spatial dependency image feature space show spatial dependency play important role relocalization task SPLSTM effectively learn information experimental result publicly available dataset show approach generalizes well outperforms recent method substantial margin Overall proposed method reduces approx 6 time position error 3 time orientation error compared current state art source code trained model released 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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['Brendt Wohlberg']

1708.09038v1

The most widely used form of convolutional sparse coding uses an $\ell_1$ regularization term. While this approach has been successful in a variety of applications, a limitation of the $\ell_1$ penalty is that it is homogeneous across the spatial and filter index dimensions of the sparse representation array, so that sparsity cannot be separately controlled across these dimensions. The present paper considers the consequences of replacing the $\ell_1$ penalty with a mixed group norm, motivated by recent theoretical results for convolutional sparse representations. Algorithms are developed for solving the resulting problems, which are quite challenging, and the impact on the performance of the denoising problem is evaluated. The mixed group norms are found to perform very poorly in this application. While their performance is greatly improved by introducing a weighting strategy, such a strategy also improves the performance obtained from the much simpler and computationally cheaper $\ell_1$ norm.

Convolutional Sparse Coding with Overlapping Group Norms

http://arxiv.org/pdf/1708.09038v1

Title Convolutional Sparse Coding Overlapping Group Norms Summary widely used form convolutional sparse coding us ell1 regularization term approach successful variety application limitation ell1 penalty homogeneous across spatial filter index dimension sparse representation array sparsity cannot separately controlled across dimension present paper considers consequence replacing ell1 penalty mixed group norm motivated recent theoretical result convolutional sparse representation Algorithms developed solving resulting problem quite challenging impact performance denoising problem evaluated mixed group norm found perform poorly application performance greatly improved introducing weighting strategy strategy also improves performance obtained much simpler computationally cheaper ell1 norm 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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['Mahdyar Ravanbakhsh', 'Moin Nabi', 'Enver Sangineto', 'Lucio Marcenaro', 'Carlo Regazzoni', 'Nicu Sebe']

1708.09644v1

In this paper we address the abnormality detection problem in crowded scenes. We propose to use Generative Adversarial Nets (GANs), which are trained using normal frames and corresponding optical-flow images in order to learn an internal representation of the scene normality. Since our GANs are trained with only normal data, they are not able to generate abnormal events. At testing time the real data are compared with both the appearance and the motion representations reconstructed by our GANs and abnormal areas are detected by computing local differences. Experimental results on challenging abnormality detection datasets show the superiority of the proposed method compared to the state of the art in both frame-level and pixel-level abnormality detection tasks.

Abnormal Event Detection in Videos using Generative Adversarial Nets

http://arxiv.org/pdf/1708.09644v1

Title Abnormal Event Detection Videos using Generative Adversarial Nets Summary paper address abnormality detection problem crowded scene propose use Generative Adversarial Nets GANs trained using normal frame corresponding opticalflow image order learn internal representation scene normality Since GANs trained normal data able generate abnormal event testing time real data compared appearance motion representation reconstructed GANs abnormal area detected computing local difference Experimental result challenging abnormality detection datasets show superiority proposed method compared state art framelevel pixellevel abnormality detection 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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['Ganghun Kim', 'Stefan Kapetanovic', 'Rachael Palmer', 'Rajesh Menon']

1709.00408v1

Machine learning (ML) has been widely applied to image classification. Here, we extend this application to data generated by a camera comprised of only a standard CMOS image sensor with no lens. We first created a database of lensless images of handwritten digits. Then, we trained a ML algorithm on this dataset. Finally, we demonstrated that the trained ML algorithm is able to classify the digits with accuracy as high as 99% for 2 digits. Our approach clearly demonstrates the potential for non-human cameras in machine-based decision-making scenarios.

Lensless-camera based machine learning for image classification

http://arxiv.org/pdf/1709.00408v1

Title Lenslesscamera based machine learning image classification Summary Machine learning ML widely applied image classification extend application data generated camera comprised standard CMOS image sensor lens first created database lensless image handwritten digit trained ML algorithm dataset Finally demonstrated trained ML algorithm able classify digit accuracy high 99 2 digit approach clearly demonstrates potential nonhuman camera machinebased decisionmaking scenario 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 Wendel', 'James Underwood']

1709.00846v3

Line scanning cameras, which capture only a single line of pixels, have been increasingly used in ground based mobile or robotic platforms. In applications where it is advantageous to directly georeference the camera data to world coordinates, an accurate estimate of the camera's 6D pose is required. This paper focuses on the common case where a mobile platform is equipped with a rigidly mounted line scanning camera, whose pose is unknown, and a navigation system providing vehicle body pose estimates. We propose a novel method that estimates the camera's pose relative to the navigation system. The approach involves imaging and manually labelling a calibration pattern with distinctly identifiable points, triangulating these points from camera and navigation system data and reprojecting them in order to compute a likelihood, which is maximised to estimate the 6D camera pose. Additionally, a Markov Chain Monte Carlo (MCMC) algorithm is used to estimate the uncertainty of the offset. Tested on two different platforms, the method was able to estimate the pose to within 0.06 m / 1.05$^{\circ}$ and 0.18 m / 2.39$^{\circ}$. We also propose several approaches to displaying and interpreting the 6D results in a human readable way.

Extrinsic Parameter Calibration for Line Scanning Cameras on Ground Vehicles with Navigation Systems Using a Calibration Pattern

http://arxiv.org/pdf/1709.00846v3

Title Extrinsic Parameter Calibration Line Scanning Cameras Ground Vehicles Navigation Systems Using Calibration Pattern Summary Line scanning camera capture single line pixel increasingly used ground based mobile robotic platform application advantageous directly georeference camera data world coordinate accurate estimate camera 6D pose required paper focus common case mobile platform equipped rigidly mounted line scanning camera whose pose unknown navigation system providing vehicle body pose estimate propose novel method estimate camera pose relative navigation system approach involves imaging manually labelling calibration pattern distinctly identifiable point triangulating point camera navigation system data reprojecting order compute likelihood maximised estimate 6D camera pose Additionally Markov Chain Monte Carlo MCMC algorithm used estimate uncertainty offset Tested two different platform method able estimate pose within 006 105circ 018 239circ also propose several approach displaying interpreting 6D result human readable way 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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['Burhan Gulbahar', 'Gorkem Memisoglu']

1709.00907v1

Single particle tracking systems monitor cellular processes with great accuracy in nano-biological systems. The emissions of the fluorescent molecules are detected with cameras or photodetectors. However, state-of-the-art imaging systems have challenges in the detection capability, collection and analysis of imaging data, penetration depth and complicated set-ups. In this article, a \textit{signaling based nanoscale acousto-optic radar and microfluidic particle tracking system} is proposed based on the theoretical design providing nanoscale optical modulator with vibrating F{\"{o}}rster resonance energy transfer (VFRET) and vibrating CdSe/ZnS quantum dots (QDs) on graphene resonators. The modulator structure combines the significant advantages of graphene membranes having wideband resonance frequencies with QDs having broad absorption spectrum and tunable properties. The solution denoted by chirp spread spectrum (CSS) Tag (\textit{CSSTag}) utilizes classical radar target tracking approaches in nanoscale environments based on the capability to generate CSS sequences to identify different bio-particles. Numerical and Monte-Carlo simulations are realized showing the significant performance for multiple particle tracking (MPT) with a modulator of $10 \, \mu$m $\times$ $10 \, \mu$m $\times$ $10 \, \mu$m dimension and several picograms of weight, signal to noise ratio (SNR) in the range $-7$ dB to $10$ dB and high speed tracking capability for microfluidic and in-body environments.

CSSTag: Optical Nanoscale Radar and Particle Tracking for In-Body and Microfluidic Systems with Vibrating Graphene and Resonance Energy Transfer

http://arxiv.org/pdf/1709.00907v1

Title CSSTag Optical Nanoscale Radar Particle Tracking InBody Microfluidic Systems Vibrating Graphene Resonance Energy Transfer Summary Single particle tracking system monitor cellular process great accuracy nanobiological system emission fluorescent molecule detected camera photodetectors However stateoftheart imaging system challenge detection capability collection analysis imaging data penetration depth complicated setup article textitsignaling based nanoscale acoustooptic radar microfluidic particle tracking system proposed based theoretical design providing nanoscale optical modulator vibrating Forster resonance energy transfer VFRET vibrating CdSeZnS quantum dot QDs graphene resonator modulator structure combine significant advantage graphene membrane wideband resonance frequency QDs broad absorption spectrum tunable property solution denoted chirp spread spectrum CSS Tag textitCSSTag utilizes classical radar target tracking approach nanoscale environment based capability generate CSS sequence identify different bioparticles Numerical MonteCarlo simulation realized showing significant performance multiple particle tracking MPT modulator 10 mum time 10 mum time 10 mum dimension several picograms weight signal noise ratio SNR range 7 dB 10 dB high speed tracking capability microfluidic inbody environment Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Daniel Schneider', 'Jens Grubert']

1709.00965v1

Smartphones are a popular device class for mobile Augmented Reality but suffer from a limited input space. Around-device interaction techniques aim at extending this input space using various sensing modalities. In this paper we present our work towards extending the input area of mobile devices using front-facing device-centered cameras that capture reflections in the cornea. As current generation mobile devices lack high resolution front-facing cameras, we study the feasibility of around-device interaction using corneal reflective imaging based on a high resolution camera. We present a workflow, a technical prototype and a feasibility evaluation.

Feasibility of Corneal Imaging for Handheld Augmented Reality

http://arxiv.org/pdf/1709.00965v1

Title Feasibility Corneal Imaging Handheld Augmented Reality Summary Smartphones popular device class mobile Augmented Reality suffer limited input space Arounddevice interaction technique aim extending input space using various sensing modality paper present work towards extending input area mobile device using frontfacing devicecentered camera capture reflection cornea current generation mobile device lack high resolution frontfacing camera study feasibility arounddevice interaction using corneal reflective imaging based high resolution camera present workflow technical prototype feasibility evaluation 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 Schneider', 'Jens Grubert']

1709.00966v1

Around-device interaction techniques aim at extending the input space using various sensing modalities on mobile and wearable devices. In this paper, we present our work towards extending the input area of mobile devices using front-facing device-centered cameras that capture reflections in the human eye. As current generation mobile devices lack high resolution front-facing cameras we study the feasibility of around-device interaction using corneal reflective imaging based on a high resolution camera. We present a workflow, a technical prototype and an evaluation, including a migration path from high resolution to low resolution imagers. Our study indicates, that under optimal conditions a spatial sensing resolution of 5 cm in the vicinity of a mobile phone is possible.

Towards Around-Device Interaction using Corneal Imaging

http://arxiv.org/pdf/1709.00966v1

Title Towards AroundDevice Interaction using Corneal Imaging Summary Arounddevice interaction technique aim extending input space using various sensing modality mobile wearable device paper present work towards extending input area mobile device using frontfacing devicecentered camera capture reflection human eye current generation mobile device lack high resolution frontfacing camera study feasibility arounddevice interaction using corneal reflective imaging based high resolution camera present workflow technical prototype evaluation including migration path high resolution low resolution imagers study indicates optimal condition spatial sensing resolution 5 cm vicinity mobile phone possible 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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['Maryam Khanian', 'Ali Sharifi Boroujerdi', 'Michael Breuß']

1709.01357v1

Photometric stereo (PS) is a fundamental technique in computer vision known to produce 3-D shape with high accuracy. The setting of PS is defined by using several input images of a static scene taken from one and the same camera position but under varying illumination. The vast majority of studies in this 3-D reconstruction method assume orthographic projection for the camera model. In addition, they mainly consider the Lambertian reflectance model as the way that light scatters at surfaces. So, providing reliable PS results from real world objects still remains a challenging task. We address 3-D reconstruction by PS using a more realistic set of assumptions combining for the first time the complete Blinn-Phong reflectance model and perspective projection. To this end, we will compare two different methods of incorporating the perspective projection into our model. Experiments are performed on both synthetic and real world images. Note that our real-world experiments do not benefit from laboratory conditions. The results show the high potential of our method even for complex real world applications such as medical endoscopy images which may include high amounts of specular highlights.

Photometric stereo for strong specular highlights

http://arxiv.org/pdf/1709.01357v1

Title Photometric stereo strong specular highlight Summary Photometric stereo PS fundamental technique computer vision known produce 3D shape high accuracy setting PS defined using several input image static scene taken one camera position varying illumination vast majority study 3D reconstruction method assume orthographic projection camera model addition mainly consider Lambertian reflectance model way light scatter surface providing reliable PS result real world object still remains challenging task address 3D reconstruction PS using realistic set assumption combining first time complete BlinnPhong reflectance model perspective projection end compare two different method incorporating perspective projection model Experiments performed synthetic real world image Note realworld experiment benefit laboratory condition result show high potential method even complex real world application medical endoscopy image may include high amount specular highlight 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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['Oscar Mendez', 'Simon Hadfield', 'Nicolas Pugeault', 'Richard Bowden']

1709.01500v1

How does a person work out their location using a floorplan? It is probably safe to say that we do not explicitly measure depths to every visible surface and try to match them against different pose estimates in the floorplan. And yet, this is exactly how most robotic scan-matching algorithms operate. Similarly, we do not extrude the 2D geometry present in the floorplan into 3D and try to align it to the real-world. And yet, this is how most vision-based approaches localise. Humans do the exact opposite. Instead of depth, we use high level semantic cues. Instead of extruding the floorplan up into the third dimension, we collapse the 3D world into a 2D representation. Evidence of this is that many of the floorplans we use in everyday life are not accurate, opting instead for high levels of discriminative landmarks. In this work, we use this insight to present a global localisation approach that relies solely on the semantic labels present in the floorplan and extracted from RGB images. While our approach is able to use range measurements if available, we demonstrate that they are unnecessary as we can achieve results comparable to state-of-the-art without them.

SeDAR - Semantic Detection and Ranging: Humans can localise without LiDAR, can robots?

http://arxiv.org/pdf/1709.01500v1

Title SeDAR Semantic Detection Ranging Humans localise without LiDAR robot Summary person work location using floorplan probably safe say explicitly measure depth every visible surface try match different pose estimate floorplan yet exactly robotic scanmatching algorithm operate Similarly extrude 2D geometry present floorplan 3D try align realworld yet visionbased approach localise Humans exact opposite Instead depth use high level semantic cue Instead extruding floorplan third dimension collapse 3D world 2D representation Evidence many floorplans use everyday life accurate opting instead high level discriminative landmark work use insight present global localisation approach relies solely semantic label present floorplan extracted RGB image approach able use range measurement available demonstrate unnecessary achieve result comparable stateoftheart without 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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['Ekta Vats', 'Anders Hast', 'Prashant Singh']

1709.01782v3

Document image binarization is often a challenging task due to various forms of degradation. Although there exist several binarization techniques in literature, the binarized image is typically sensitive to control parameter settings of the employed technique. This paper presents an automatic document image binarization algorithm to segment the text from heavily degraded document images. The proposed technique uses a two band-pass filtering approach for background noise removal, and Bayesian optimization for automatic hyperparameter selection for optimal results. The effectiveness of the proposed binarization technique is empirically demonstrated on the Document Image Binarization Competition (DIBCO) and the Handwritten Document Image Binarization Competition (H-DIBCO) datasets.

Automatic Document Image Binarization using Bayesian Optimization

http://arxiv.org/pdf/1709.01782v3

Title Automatic Document Image Binarization using Bayesian Optimization Summary Document image binarization often challenging task due various form degradation Although exist several binarization technique literature binarized image typically sensitive control parameter setting employed technique paper present automatic document image binarization algorithm segment text heavily degraded document image proposed technique us two bandpass filtering approach background noise removal Bayesian optimization automatic hyperparameter selection optimal result effectiveness proposed binarization technique empirically demonstrated Document Image Binarization Competition DIBCO Handwritten Document Image Binarization Competition HDIBCO 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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['Anders Hast', 'Ekta Vats']

1709.01788v4

Automatic recognition of historical handwritten manuscripts is a daunting task due to paper degradation over time. Recognition-free retrieval or word spotting is popularly used for information retrieval and digitization of the historical handwritten documents. However, the performance of word spotting algorithms depends heavily on feature detection and representation methods. Although there exist popular feature descriptors such as Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF), the invariant properties of these descriptors amplify the noise in the degraded document images, rendering them more sensitive to noise and complex characteristics of historical manuscripts. Therefore, an efficient and relaxed feature descriptor is required as handwritten words across different documents are indeed similar, but not identical. This paper introduces a Radial Line Fourier (RLF) descriptor for handwritten word representation, with a short feature vector of 32 dimensions. A segmentation-free and training-free handwritten word spotting method is studied herein that relies on the proposed RLF descriptor, takes into account different keypoint representations and uses a simple preconditioner-based feature matching algorithm. The effectiveness of the RLF descriptor for segmentation-free handwritten word spotting is empirically evaluated on well-known historical handwritten datasets using standard evaluation measures.

Radial Line Fourier Descriptor for Historical Handwritten Text Representation

http://arxiv.org/pdf/1709.01788v4

Title Radial Line Fourier Descriptor Historical Handwritten Text Representation Summary Automatic recognition historical handwritten manuscript daunting task due paper degradation time Recognitionfree retrieval word spotting popularly used information retrieval digitization historical handwritten document However performance word spotting algorithm depends heavily feature detection representation method Although exist popular feature descriptor Scale Invariant Feature Transform SIFT Speeded Robust Features SURF invariant property descriptor amplify noise degraded document image rendering sensitive noise complex characteristic historical manuscript Therefore efficient relaxed feature descriptor required handwritten word across different document indeed similar identical paper introduces Radial Line Fourier RLF descriptor handwritten word representation short feature vector 32 dimension segmentationfree trainingfree handwritten word spotting method studied herein relies proposed RLF descriptor take account different keypoint representation us simple preconditionerbased feature matching algorithm effectiveness RLF descriptor segmentationfree handwritten word spotting empirically evaluated wellknown historical handwritten datasets using standard evaluation measure 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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['Surat Teerapittayanon', 'Bradley McDanel', 'H. T. Kung']

1709.01921v1

We propose distributed deep neural networks (DDNNs) over distributed computing hierarchies, consisting of the cloud, the edge (fog) and end devices. While being able to accommodate inference of a deep neural network (DNN) in the cloud, a DDNN also allows fast and localized inference using shallow portions of the neural network at the edge and end devices. When supported by a scalable distributed computing hierarchy, a DDNN can scale up in neural network size and scale out in geographical span. Due to its distributed nature, DDNNs enhance sensor fusion, system fault tolerance and data privacy for DNN applications. In implementing a DDNN, we map sections of a DNN onto a distributed computing hierarchy. By jointly training these sections, we minimize communication and resource usage for devices and maximize usefulness of extracted features which are utilized in the cloud. The resulting system has built-in support for automatic sensor fusion and fault tolerance. As a proof of concept, we show a DDNN can exploit geographical diversity of sensors to improve object recognition accuracy and reduce communication cost. In our experiment, compared with the traditional method of offloading raw sensor data to be processed in the cloud, DDNN locally processes most sensor data on end devices while achieving high accuracy and is able to reduce the communication cost by a factor of over 20x.

Distributed Deep Neural Networks over the Cloud, the Edge and End Devices

http://arxiv.org/pdf/1709.01921v1

Title Distributed Deep Neural Networks Cloud Edge End Devices Summary propose distributed deep neural network DDNNs distributed computing hierarchy consisting cloud edge fog end device able accommodate inference deep neural network DNN cloud DDNN also allows fast localized inference using shallow portion neural network edge end device supported scalable distributed computing hierarchy DDNN scale neural network size scale geographical span Due distributed nature DDNNs enhance sensor fusion system fault tolerance data privacy DNN application implementing DDNN map section DNN onto distributed computing hierarchy jointly training section minimize communication resource usage device maximize usefulness extracted feature utilized cloud resulting system builtin support automatic sensor fusion fault tolerance proof concept show DDNN exploit geographical diversity sensor improve object recognition accuracy reduce communication cost experiment compared traditional method offloading raw sensor data processed cloud DDNN locally process sensor data end device achieving high accuracy able reduce communication cost factor 20x 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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['Matias Valdenegro-Toro']

1709.02150v1

Matching sonar images with high accuracy has been a problem for a long time, as sonar images are inherently hard to model due to reflections, noise and viewpoint dependence. Autonomous Underwater Vehicles require good sonar image matching capabilities for tasks such as tracking, simultaneous localization and mapping (SLAM) and some cases of object detection/recognition. We propose the use of Convolutional Neural Networks (CNN) to learn a matching function that can be trained from labeled sonar data, after pre-processing to generate matching and non-matching pairs. In a dataset of 39K training pairs, we obtain 0.91 Area under the ROC Curve (AUC) for a CNN that outputs a binary classification matching decision, and 0.89 AUC for another CNN that outputs a matching score. In comparison, classical keypoint matching methods like SIFT, SURF, ORB and AKAZE obtain AUC 0.61 to 0.68. Alternative learning methods obtain similar results, with a Random Forest Classifier obtaining AUC 0.79, and a Support Vector Machine resulting in AUC 0.66.

Improving Sonar Image Patch Matching via Deep Learning

http://arxiv.org/pdf/1709.02150v1

Title Improving Sonar Image Patch Matching via Deep Learning Summary Matching sonar image high accuracy problem long time sonar image inherently hard model due reflection noise viewpoint dependence Autonomous Underwater Vehicles require good sonar image matching capability task tracking simultaneous localization mapping SLAM case object detectionrecognition propose use Convolutional Neural Networks CNN learn matching function trained labeled sonar data preprocessing generate matching nonmatching pair dataset 39K training pair obtain 091 Area ROC Curve AUC CNN output binary classification matching decision 089 AUC another CNN output matching score comparison classical keypoint matching method like SIFT SURF ORB AKAZE obtain AUC 061 068 Alternative learning method obtain similar result Random Forest Classifier obtaining AUC 079 Support Vector Machine resulting AUC 066 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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['Matias Valdenegro-Toro']

1709.02153v1

Deep Neural Networks have impressive classification performance, but this comes at the expense of significant computational resources at inference time. Autonomous Underwater Vehicles use low-power embedded systems for sonar image perception, and cannot execute large neural networks in real-time. We propose the use of max-pooling aggressively, and we demonstrate it with a Fire-based module and a new Tiny module that includes max-pooling in each module. By stacking them we build networks that achieve the same accuracy as bigger ones, while reducing the number of parameters and considerably increasing computational performance. Our networks can classify a 96x96 sonar image with 98.8 - 99.7 accuracy on only 41 to 61 milliseconds on a Raspberry Pi 2, which corresponds to speedups of 28.6 - 19.7.

Real-time convolutional networks for sonar image classification in low-power embedded systems

http://arxiv.org/pdf/1709.02153v1

Title Realtime convolutional network sonar image classification lowpower embedded system Summary Deep Neural Networks impressive classification performance come expense significant computational resource inference time Autonomous Underwater Vehicles use lowpower embedded system sonar image perception cannot execute large neural network realtime propose use maxpooling aggressively demonstrate Firebased module new Tiny module includes maxpooling module stacking build network achieve accuracy bigger one reducing number parameter considerably increasing computational performance network classify 96x96 sonar image 988 997 accuracy 41 61 millisecond Raspberry Pi 2 corresponds speedup 286 197 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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['Patricia Binder', 'Michael Muma', 'Abdelhak M. Zoubir']

1709.02287v1

Distributed signal processing for wireless sensor networks enables that different devices cooperate to solve different signal processing tasks. A crucial first step is to answer the question: who observes what? Recently, several distributed algorithms have been proposed, which frame the signal/object labelling problem in terms of cluster analysis after extracting source-specific features, however, the number of clusters is assumed to be known. We propose a new method called Gravitational Clustering (GC) to adaptively estimate the time-varying number of clusters based on a set of feature vectors. The key idea is to exploit the physical principle of gravitational force between mass units: streaming-in feature vectors are considered as mass units of fixed position in the feature space, around which mobile mass units are injected at each time instant. The cluster enumeration exploits the fact that the highest attraction on the mobile mass units is exerted by regions with a high density of feature vectors, i.e., gravitational clusters. By sharing estimates among neighboring nodes via a diffusion-adaptation scheme, cooperative and distributed cluster enumeration is achieved. Numerical experiments concerning robustness against outliers, convergence and computational complexity are conducted. The application in a distributed cooperative multi-view camera network illustrates the applicability to real-world problems.

Gravitational Clustering: A Simple, Robust and Adaptive Approach for Distributed Networks

http://arxiv.org/pdf/1709.02287v1

Title Gravitational Clustering Simple Robust Adaptive Approach Distributed Networks Summary Distributed signal processing wireless sensor network enables different device cooperate solve different signal processing task crucial first step answer question observes Recently several distributed algorithm proposed frame signalobject labelling problem term cluster analysis extracting sourcespecific feature however number cluster assumed known propose new method called Gravitational Clustering GC adaptively estimate timevarying number cluster based set feature vector key idea exploit physical principle gravitational force mass unit streamingin feature vector considered mass unit fixed position feature space around mobile mass unit injected time instant cluster enumeration exploit fact highest attraction mobile mass unit exerted region high density feature vector ie gravitational cluster sharing estimate among neighboring node via diffusionadaptation scheme cooperative distributed cluster enumeration achieved Numerical experiment concerning robustness outlier convergence computational complexity conducted application distributed cooperative multiview camera network illustrates applicability realworld problem Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Timnit Gebru', 'Jonathan Krause', 'Jia Deng', 'Li Fei-Fei']

1709.02482v1

We present a crowdsourcing workflow to collect image annotations for visually similar synthetic categories without requiring experts. In animals, there is a direct link between taxonomy and visual similarity: e.g. a collie (type of dog) looks more similar to other collies (e.g. smooth collie) than a greyhound (another type of dog). However, in synthetic categories such as cars, objects with similar taxonomy can have very different appearance: e.g. a 2011 Ford F-150 Supercrew-HD looks the same as a 2011 Ford F-150 Supercrew-LL but very different from a 2011 Ford F-150 Supercrew-SVT. We introduce a graph based crowdsourcing algorithm to automatically group visually indistinguishable objects together. Using our workflow, we label 712,430 images by ~1,000 Amazon Mechanical Turk workers; resulting in the largest fine-grained visual dataset reported to date with 2,657 categories of cars annotated at 1/20th the cost of hiring experts.

Scalable Annotation of Fine-Grained Categories Without Experts

http://arxiv.org/pdf/1709.02482v1

Title Scalable Annotation FineGrained Categories Without Experts Summary present crowdsourcing workflow collect image annotation visually similar synthetic category without requiring expert animal direct link taxonomy visual similarity eg collie type dog look similar collie eg smooth collie greyhound another type dog However synthetic category car object similar taxonomy different appearance eg 2011 Ford F150 SupercrewHD look 2011 Ford F150 SupercrewLL different 2011 Ford F150 SupercrewSVT introduce graph based crowdsourcing algorithm automatically group visually indistinguishable object together Using workflow label 712430 image 1000 Amazon Mechanical Turk worker resulting largest finegrained visual dataset reported date 2657 category car annotated 120th cost hiring expert 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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