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['Birgitta Dresp-Langley', 'John Mwangi Wandeto']

1710.10648v1

The quantization error (QE) from SOM applied on time series of spatial contrast images with variable relative amount of white and dark pixel contents, as in monochromatic medical images or satellite images, is proven a reliable indicator of potentially critical changes in image homogeneity. The QE is shown to increase linearly with the variability in spatial contrast contents across time when contrast intensity is kept constant.

Using the quantization error from Self-Organized Map (SOM) output for detecting critical variability in large bodies of image time series in less than a minute

http://arxiv.org/pdf/1710.10648v1

Title Using quantization error SelfOrganized Map SOM output detecting critical variability large body image time series le minute Summary quantization error QE SOM applied time series spatial contrast image variable relative amount white dark pixel content monochromatic medical image satellite image proven reliable indicator potentially critical change image homogeneity QE shown increase linearly variability spatial contrast content across time contrast intensity kept constant Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Niels Chr. Overgaard']

1710.10985v1

A new proof of the equivalence of the Taut String Algorithm and the one-dimensional Rudin-Osher-Fatemi model is presented. Based on duality and the projection theorem in Hilbert space, the proof is strictly elementary. Existence and uniqueness of solutions to both denoising models follow as by-products. The standard convergence properties of the denoised signal, as the regularizing parameter tends to zero, are recalled and efficient proofs provided. Moreover, a new and fundamental bound on the denoised signal is derived. This bound implies, among other things, the strong convergence (in the space of functions of bounded variation) of the denoised signal to the insignal as the regularization parameter vanishes. The methods developed in the paper can be modified to cover other interesting applications such as isotonic regression.

On the Taut String Interpretation of the One-dimensional Rudin-Osher-Fatemi Model: A New Proof, a Fundamental Estimate and Some Applications

http://arxiv.org/pdf/1710.10985v1

Title Taut String Interpretation Onedimensional RudinOsherFatemi Model New Proof Fundamental Estimate Applications Summary new proof equivalence Taut String Algorithm onedimensional RudinOsherFatemi model presented Based duality projection theorem Hilbert space proof strictly elementary Existence uniqueness solution denoising model follow byproduct standard convergence property denoised signal regularizing parameter tends zero recalled efficient proof provided Moreover new fundamental bound denoised signal derived bound implies among thing strong convergence space function bounded variation denoised signal insignal regularization parameter vanishes method developed paper modified cover interesting application isotonic regression Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hyomin Choi', 'Ivan V. Bajic']

1710.11151v2

Image and video compression has traditionally been tailored to human vision. However, modern applications such as visual analytics and surveillance rely on computers seeing and analyzing the images before (or instead of) humans. For these applications, it is important to adjust compression to computer vision. In this paper we present a bit allocation and rate control strategy that is tailored to object detection. Using the initial convolutional layers of a state-of-the-art object detector, we create an importance map that can guide bit allocation to areas that are important for object detection. The proposed method enables bit rate savings of 7% or more compared to default HEVC, at the equivalent object detection rate.

High efficiency compression for object detection

http://arxiv.org/pdf/1710.11151v2

Title High efficiency compression object detection Summary Image video compression traditionally tailored human vision However modern application visual analytics surveillance rely computer seeing analyzing image instead human application important adjust compression computer vision paper present bit allocation rate control strategy tailored object detection Using initial convolutional layer stateoftheart object detector create importance map guide bit allocation area important object detection proposed method enables bit rate saving 7 compared default HEVC equivalent object detection rate Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mohammad Babaeizadeh', 'Chelsea Finn', 'Dumitru Erhan', 'Roy H. Campbell', 'Sergey Levine']

1710.11252v2

Predicting the future in real-world settings, particularly from raw sensory observations such as images, is exceptionally challenging. Real-world events can be stochastic and unpredictable, and the high dimensionality and complexity of natural images requires the predictive model to build an intricate understanding of the natural world. Many existing methods tackle this problem by making simplifying assumptions about the environment. One common assumption is that the outcome is deterministic and there is only one plausible future. This can lead to low-quality predictions in real-world settings with stochastic dynamics. In this paper, we develop a stochastic variational video prediction (SV2P) method that predicts a different possible future for each sample of its latent variables. To the best of our knowledge, our model is the first to provide effective stochastic multi-frame prediction for real-world video. We demonstrate the capability of the proposed method in predicting detailed future frames of videos on multiple real-world datasets, both action-free and action-conditioned. We find that our proposed method produces substantially improved video predictions when compared to the same model without stochasticity, and to other stochastic video prediction methods. Our SV2P implementation will be open sourced upon publication.

Stochastic Variational Video Prediction

http://arxiv.org/pdf/1710.11252v2

Title Stochastic Variational Video Prediction Summary Predicting future realworld setting particularly raw sensory observation image exceptionally challenging Realworld event stochastic unpredictable high dimensionality complexity natural image requires predictive model build intricate understanding natural world Many existing method tackle problem making simplifying assumption environment One common assumption outcome deterministic one plausible future lead lowquality prediction realworld setting stochastic dynamic paper develop stochastic variational video prediction SV2P method predicts different possible future sample latent variable best knowledge model first provide effective stochastic multiframe prediction realworld video demonstrate capability proposed method predicting detailed future frame video multiple realworld datasets actionfree actionconditioned find proposed method produce substantially improved video prediction compared model without stochasticity stochastic video prediction method SV2P implementation open sourced upon publication Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yao Yao', 'Mustafa Mehmet Ali', 'Shahin Vakilinia']

1710.11454v1

This paper is concerned with optimization of distributed broadband wireless communication (BWC) systems. BWC systems contain a distributed antenna system (DAS) connected to a base station with optical fiber. Distributed BWC systems have been proposed as a solution to the power constraint problem in traditional cellular networks. So far, the research on BWC systems have advanced on two separate tracks, the design of the system to meet the quality of service requirements (QoS) and optimization of the location of the DAS. In this paper, we consider a combined optimization of BWC systems. We consider uplink communications in distributed BWC systems with multiple levels of priority traffic with arrivals and departures forming renewal processes. We develop an analysis that determines packet delay violation probability for each priority level as a function of the outage probability of the DAS through the application of results from renewal theory. Then, we determine the optimal locations of the antennas that minimize the antenna outage probability. We also study the tradeoff between the packet delay violation probability and packet loss probability. This work will be helpful in the designing of the distributed BWC systems.

Optimal Resource Allocation in Distributed Broadband Wireless Communication Systems

http://arxiv.org/pdf/1710.11454v1

Title Optimal Resource Allocation Distributed Broadband Wireless Communication Systems Summary paper concerned optimization distributed broadband wireless communication BWC system BWC system contain distributed antenna system DAS connected base station optical fiber Distributed BWC system proposed solution power constraint problem traditional cellular network far research BWC system advanced two separate track design system meet quality service requirement QoS optimization location DAS paper consider combined optimization BWC system consider uplink communication distributed BWC system multiple level priority traffic arrival departure forming renewal process develop analysis determines packet delay violation probability priority level function outage probability DAS application result renewal theory determine optimal location antenna minimize antenna outage probability also study tradeoff packet delay violation probability packet loss probability work helpful designing distributed BWC 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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['Yu Xiang', 'Tanner Schmidt', 'Venkatraman Narayanan', 'Dieter Fox']

1711.00199v2

Estimating the 6D pose of known objects is important for robots to interact with the real world. The problem is challenging due to the variety of objects as well as the complexity of a scene caused by clutter and occlusions between objects. In this work, we introduce PoseCNN, a new Convolutional Neural Network for 6D object pose estimation. PoseCNN estimates the 3D translation of an object by localizing its center in the image and predicting its distance from the camera. The 3D rotation of the object is estimated by regressing to a quaternion representation. We also introduce a novel loss function that enables PoseCNN to handle symmetric objects. In addition, we contribute a large scale video dataset for 6D object pose estimation named the YCB-Video dataset. Our dataset provides accurate 6D poses of 21 objects from the YCB dataset observed in 92 videos with 133,827 frames. We conduct extensive experiments on our YCB-Video dataset and the OccludedLINEMOD dataset to show that PoseCNN is highly robust to occlusions, can handle symmetric objects, and provide accurate pose estimation using only color images as input. When using depth data to further refine the poses, our approach achieves state-of-the-art results on the challenging OccludedLINEMOD dataset.

PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes

http://arxiv.org/pdf/1711.00199v2

Title PoseCNN Convolutional Neural Network 6D Object Pose Estimation Cluttered Scenes Summary Estimating 6D pose known object important robot interact real world problem challenging due variety object well complexity scene caused clutter occlusion object work introduce PoseCNN new Convolutional Neural Network 6D object pose estimation PoseCNN estimate 3D translation object localizing center image predicting distance camera 3D rotation object estimated regressing quaternion representation also introduce novel loss function enables PoseCNN handle symmetric object addition contribute large scale video dataset 6D object pose estimation named YCBVideo dataset dataset provides accurate 6D pose 21 object YCB dataset observed 92 video 133827 frame conduct extensive experiment YCBVideo dataset OccludedLINEMOD dataset show PoseCNN highly robust occlusion handle symmetric object provide accurate pose estimation using color image input using depth data refine pose approach achieves stateoftheart result challenging OccludedLINEMOD dataset Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Do-Guk Kim', 'Jong-Uk Hou', 'Heung-Kyu Lee']

1711.00207v1

Color laser printers have fast printing speed and high resolution, and forgeries using color laser printers can cause significant harm to society. A source printer identification technique can be employed as a countermeasure to those forgeries. This paper presents a color laser printer identification method based on cascaded learning of deep neural networks. The refiner network is trained by adversarial training to refine the synthetic dataset for halftone color decomposition. The halftone color decomposing ConvNet is trained with the refined dataset, and the trained knowledge is transferred to the printer identifying ConvNet to enhance the accuracy. The robustness about rotation and scaling is considered in training process, which is not considered in existing methods. Experiments are performed on eight color laser printers, and the performance is compared with several existing methods. The experimental results clearly show that the proposed method outperforms existing source color laser printer identification methods.

Learning deep features for source color laser printer identification based on cascaded learning

http://arxiv.org/pdf/1711.00207v1

Title Learning deep feature source color laser printer identification based cascaded learning Summary Color laser printer fast printing speed high resolution forgery using color laser printer cause significant harm society source printer identification technique employed countermeasure forgery paper present color laser printer identification method based cascaded learning deep neural network refiner network trained adversarial training refine synthetic dataset halftone color decomposition halftone color decomposing ConvNet trained refined dataset trained knowledge transferred printer identifying ConvNet enhance accuracy robustness rotation scaling considered training process considered existing method Experiments performed eight color laser printer performance compared several existing method experimental result clearly show proposed method outperforms existing source color laser printer identification 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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['Zhuoxiang Chen', 'Zhe Xu', 'Ya Zhang', 'Xiao Gu']

1711.00248v1

Image-based clothing retrieval is receiving increasing interest with the growth of online shopping. In practice, users may often have a desired piece of clothing in mind (e.g., either having seen it before on the street or requiring certain specific clothing attributes) but may be unable to supply an image as a query. We model this problem as a new type of image retrieval task in which the target image resides only in the user's mind (called "mental image retrieval" hereafter). Because of the absence of an explicit query image, we propose to solve this problem through relevance feedback. Specifically, a new Bayesian formulation is proposed that simultaneously models the retrieval target and its high-level representation in the mind of the user (called the "user metric" hereafter) as posterior distributions of pre-fetched shop images and heterogeneous features extracted from multiple clothing attributes, respectively. Requiring only clicks as user feedback, the proposed algorithm is able to account for the variability in human decision-making. Experiments with real users demonstrate the effectiveness of the proposed algorithm.

Query-free Clothing Retrieval via Implicit Relevance Feedback

http://arxiv.org/pdf/1711.00248v1

Title Queryfree Clothing Retrieval via Implicit Relevance Feedback Summary Imagebased clothing retrieval receiving increasing interest growth online shopping practice user may often desired piece clothing mind eg either seen street requiring certain specific clothing attribute may unable supply image query model problem new type image retrieval task target image resides user mind called mental image retrieval hereafter absence explicit query image propose solve problem relevance feedback Specifically new Bayesian formulation proposed simultaneously model retrieval target highlevel representation mind user called user metric hereafter posterior distribution prefetched shop image heterogeneous feature extracted multiple clothing attribute respectively Requiring click user feedback proposed algorithm able account variability human decisionmaking Experiments real user demonstrate effectiveness proposed algorithm Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Chenyang Shen', 'Yesenia Gonzalez', 'Liyuan Chen', 'Steve B. Jiang', 'Xun Jia']

1711.00414v1

A number of image-processing problems can be formulated as optimization problems. The objective function typically contains several terms specifically designed for different purposes. Parameters in front of these terms are used to control the relative weights among them. It is of critical importance to tune these parameters, as quality of the solution depends on their values. Tuning parameter is a relatively straightforward task for a human, as one can intelligently determine the direction of parameter adjustment based on the solution quality. Yet manual parameter tuning is not only tedious in many cases, but becomes impractical when a number of parameters exist in a problem. Aiming at solving this problem, this paper proposes an approach that employs deep reinforcement learning to train a system that can automatically adjust parameters in a human-like manner. We demonstrate our idea in an example problem of optimization-based iterative CT reconstruction with a pixel-wise total-variation regularization term. We set up a parameter tuning policy network (PTPN), which maps an CT image patch to an output that specifies the direction and amplitude by which the parameter at the patch center is adjusted. We train the PTPN via an end-to-end reinforcement learning procedure. We demonstrate that under the guidance of the trained PTPN for parameter tuning at each pixel, reconstructed CT images attain quality similar or better than in those reconstructed with manually tuned parameters.

Intelligent Parameter Tuning in Optimization-based Iterative CT Reconstruction via Deep Reinforcement Learning

http://arxiv.org/pdf/1711.00414v1

Title Intelligent Parameter Tuning Optimizationbased Iterative CT Reconstruction via Deep Reinforcement Learning Summary number imageprocessing problem formulated optimization problem objective function typically contains several term specifically designed different purpose Parameters front term used control relative weight among critical importance tune parameter quality solution depends value Tuning parameter relatively straightforward task human one intelligently determine direction parameter adjustment based solution quality Yet manual parameter tuning tedious many case becomes impractical number parameter exist problem Aiming solving problem paper proposes approach employ deep reinforcement learning train system automatically adjust parameter humanlike manner demonstrate idea example problem optimizationbased iterative CT reconstruction pixelwise totalvariation regularization term set parameter tuning policy network PTPN map CT image patch output specifies direction amplitude parameter patch center adjusted train PTPN via endtoend reinforcement learning procedure demonstrate guidance trained PTPN parameter tuning pixel reconstructed CT image attain quality similar better reconstructed manually tuned 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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['Shubham Jain', 'Marco Gruteser']

1711.00558v1

As smartphone rooted distractions become commonplace, the lack of compelling safety measures has led to a rise in the number of injuries to distracted walkers. Various solutions address this problem by sensing a pedestrian's walking environment. Existing camera-based approaches have been largely limited to obstacle detection and other forms of object detection. Instead, we present TerraFirma, an approach that performs material recognition on the pedestrian's walking surface. We explore, first, how well commercial off-the-shelf smartphone cameras can learn texture to distinguish among paving materials in uncontrolled outdoor urban settings. Second, we aim at identifying when a distracted user is about to enter the street, which can be used to support safety functions such as warning the user to be cautious. To this end, we gather a unique dataset of street/sidewalk imagery from a pedestrian's perspective, that spans major cities like New York, Paris, and London. We demonstrate that modern phone cameras can be enabled to distinguish materials of walking surfaces in urban areas with more than 90% accuracy, and accurately identify when pedestrians transition from sidewalk to street.

Recognizing Textures with Mobile Cameras for Pedestrian Safety Applications

http://arxiv.org/pdf/1711.00558v1

Title Recognizing Textures Mobile Cameras Pedestrian Safety Applications Summary smartphone rooted distraction become commonplace lack compelling safety measure led rise number injury distracted walker Various solution address problem sensing pedestrian walking environment Existing camerabased approach largely limited obstacle detection form object detection Instead present TerraFirma approach performs material recognition pedestrian walking surface explore first well commercial offtheshelf smartphone camera learn texture distinguish among paving material uncontrolled outdoor urban setting Second aim identifying distracted user enter street used support safety function warning user cautious end gather unique dataset streetsidewalk imagery pedestrian perspective span major city like New York Paris London demonstrate modern phone camera enabled distinguish material walking surface urban area 90 accuracy accurately identify pedestrian transition sidewalk street Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Björn Barz', 'Joachim Denzler']

1711.00953v1

Query images presented to content-based image retrieval systems often have various different interpretations, making it difficult to identify the search objective pursued by the user. We propose a technique for overcoming this ambiguity, while keeping the amount of required user interaction at a minimum. To achieve this, the neighborhood of the query image is divided into coherent clusters from which the user may choose the relevant ones. A novel feedback integration technique is then employed to re-rank the entire database with regard to both the user feedback and the original query. We evaluate our approach on the publicly available MIRFLICKR-25K dataset, where it leads to a relative improvement of average precision by 23% over the baseline retrieval, which does not distinguish between different image senses.

Automatic Query Image Disambiguation for Content-Based Image Retrieval

http://arxiv.org/pdf/1711.00953v1

Title Automatic Query Image Disambiguation ContentBased Image Retrieval Summary Query image presented contentbased image retrieval system often various different interpretation making difficult identify search objective pursued user propose technique overcoming ambiguity keeping amount required user interaction minimum achieve neighborhood query image divided coherent cluster user may choose relevant one novel feedback integration technique employed rerank entire database regard user feedback original query evaluate approach publicly available MIRFLICKR25K dataset lead relative improvement average precision 23 baseline retrieval distinguish different image sens Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Behnaz Rezaei', 'Sarah Ostadabbas']

1711.01218v1

Background subtraction is the primary task of the majority of video inspection systems. The most important part of the background subtraction which is common among different algorithms is background modeling. In this regard, our paper addresses the problem of background modeling in a computationally efficient way, which is important for current eruption of "big data" processing coming from high resolution multi-channel videos. Our model is based on the assumption that background in natural images lies on a low-dimensional subspace. We formulated and solved this problem in a low-rank matrix completion framework. In modeling the background, we benefited from the in-face extended Frank-Wolfe algorithm for solving a defined convex optimization problem. We evaluated our fast robust matrix completion (fRMC) method on both background models challenge (BMC) and Stuttgart artificial background subtraction (SABS) datasets. The results were compared with the robust principle component analysis (RPCA) and low-rank robust matrix completion (RMC) methods, both solved by inexact augmented Lagrangian multiplier (IALM). The results showed faster computation, at least twice as when IALM solver is used, while having a comparable accuracy even better in some challenges, in subtracting the backgrounds in order to detect moving objects in the scene.

Background Subtraction via Fast Robust Matrix Completion

http://arxiv.org/pdf/1711.01218v1

Title Background Subtraction via Fast Robust Matrix Completion Summary Background subtraction primary task majority video inspection system important part background subtraction common among different algorithm background modeling regard paper address problem background modeling computationally efficient way important current eruption big data processing coming high resolution multichannel video model based assumption background natural image lie lowdimensional subspace formulated solved problem lowrank matrix completion framework modeling background benefited inface extended FrankWolfe algorithm solving defined convex optimization problem evaluated fast robust matrix completion fRMC method background model challenge BMC Stuttgart artificial background subtraction SABS datasets result compared robust principle component analysis RPCA lowrank robust matrix completion RMC method solved inexact augmented Lagrangian multiplier IALM result showed faster computation least twice IALM solver used comparable accuracy even better challenge subtracting background order detect moving object scene Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Katherine L. Bouman', 'Michael D. Johnson', 'Adrian V. Dalca', 'Andrew A. Chael', 'Freek Roelofs', 'Sheperd S. Doeleman', 'William T. Freeman']

1711.01357v2

Very long baseline interferometry (VLBI) makes it possible to recover images of astronomical sources with extremely high angular resolution. Most recently, the Event Horizon Telescope (EHT) has extended VLBI to short millimeter wavelengths with a goal of achieving angular resolution sufficient for imaging the event horizons of nearby supermassive black holes. VLBI provides measurements related to the underlying source image through a sparse set spatial frequencies. An image can then be recovered from these measurements by making assumptions about the underlying image. One of the most important assumptions made by conventional imaging methods is that over the course of a night's observation the image is static. However, for quickly evolving sources, such as the galactic center's supermassive black hole (Sgr A*) targeted by the EHT, this assumption is violated and these conventional imaging approaches fail. In this work we propose a new way to model VLBI measurements that allows us to recover both the appearance and dynamics of an evolving source by reconstructing a video rather than a static image. By modeling VLBI measurements using a Gaussian Markov Model, we are able to propagate information across observations in time to reconstruct a video, while simultaneously learning about the dynamics of the source's emission region. We demonstrate our proposed Expectation-Maximization (EM) algorithm, StarWarps, on realistic synthetic observations of black holes, and show how it substantially improves results compared to conventional imaging algorithms. Additionally, we demonstrate StarWarps on real VLBI data of the M87 Jet from the VLBA.

Reconstructing Video from Interferometric Measurements of Time-Varying Sources

http://arxiv.org/pdf/1711.01357v2

Title Reconstructing Video Interferometric Measurements TimeVarying Sources Summary long baseline interferometry VLBI make possible recover image astronomical source extremely high angular resolution recently Event Horizon Telescope EHT extended VLBI short millimeter wavelength goal achieving angular resolution sufficient imaging event horizon nearby supermassive black hole VLBI provides measurement related underlying source image sparse set spatial frequency image recovered measurement making assumption underlying image One important assumption made conventional imaging method course night observation image static However quickly evolving source galactic center supermassive black hole Sgr targeted EHT assumption violated conventional imaging approach fail work propose new way model VLBI measurement allows u recover appearance dynamic evolving source reconstructing video rather static image modeling VLBI measurement using Gaussian Markov Model able propagate information across observation time reconstruct video simultaneously learning dynamic source emission region demonstrate proposed ExpectationMaximization EM algorithm StarWarps realistic synthetic observation black hole show substantially improves result compared conventional imaging algorithm Additionally demonstrate StarWarps real VLBI data M87 Jet VLBA Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jianshu Zhang', 'Yixing Zhu', 'Jun Du', 'Lirong Dai']

1711.01889v1

Chinese characters have a huge set of character categories, more than 20,000 and the number is still increasing as more and more novel characters continue being created. However, the enormous characters can be decomposed into a few fundamental structural radicals, only about 500. This paper introduces the Radical Analysis Networks (RAN) that recognize Chinese characters by identifying radicals and analyzing 2D spatial structures between them. The proposed RAN first extracts visual features from Chinese character input by employing convolutional neural networks as an encoder. Then a decoder based on recurrent neural networks is employed, who aims to generate a caption of Chinese character by detecting radicals and 2D structures through a spatial attention mechanism. The manner of treating Chinese character input as a composition of radicals rather than a single picture severely reduces the size of vocabulary and enables RAN to possess the ability of recognizing unseen Chinese character classes only if their radicals have been seen, called zero-shot learning. We test a simple implementation of RAN on experiments of recognizing printed Chinese characters with seen and unseen classes and RAN simultaneously obtains convincing performance on unseen classes and state-of-the-art performance on seen classes.

RAN: Radical analysis networks for zero-shot learning of Chinese characters

http://arxiv.org/pdf/1711.01889v1

Title RAN Radical analysis network zeroshot learning Chinese character Summary Chinese character huge set character category 20000 number still increasing novel character continue created However enormous character decomposed fundamental structural radical 500 paper introduces Radical Analysis Networks RAN recognize Chinese character identifying radical analyzing 2D spatial structure proposed RAN first extract visual feature Chinese character input employing convolutional neural network encoder decoder based recurrent neural network employed aim generate caption Chinese character detecting radical 2D structure spatial attention mechanism manner treating Chinese character input composition radical rather single picture severely reduces size vocabulary enables RAN posse ability recognizing unseen Chinese character class radical seen called zeroshot learning test simple implementation RAN experiment recognizing printed Chinese character seen unseen class RAN simultaneously obtains convincing performance unseen class stateoftheart performance seen class Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Al-Hussein A. El-Shafie', 'S. E. D. Habib']

1711.02441v1

Visual object tracking is an active topic in the computer vision domain with applications extending over numerous fields. The main sub-tasks required to build an object tracker (e.g. object detection, feature extraction and object tracking) are computation-intensive. In addition, real-time operation of the tracker is indispensable for almost all of its applications. Therefore, complete hardware or hardware/software co-design approaches are pursued for better tracker implementations. This paper presents a literature survey of the hardware implementations of object trackers over the last two decades. Although several tracking surveys exist in literature, a survey addressing the hardware implementations of the different trackers is missing. We believe this survey would fill the gap and complete the picture with the existing surveys of how to design an efficient tracker and point out the future directions researchers can follow in this field. We highlight the lack of hardware implementations for state-of-the-art tracking algorithms as well as for enhanced classical algorithms. We also stress the need for measuring the tracking performance of the hardware-based trackers. Additionally, enough details of the hardware-based trackers need to be provided to allow reasonable comparison between the different implementations.

A Survey on Hardware Implementations of Visual Object Trackers

http://arxiv.org/pdf/1711.02441v1

Title Survey Hardware Implementations Visual Object Trackers Summary Visual object tracking active topic computer vision domain application extending numerous field main subtasks required build object tracker eg object detection feature extraction object tracking computationintensive addition realtime operation tracker indispensable almost application Therefore complete hardware hardwaresoftware codesign approach pursued better tracker implementation paper present literature survey hardware implementation object tracker last two decade Although several tracking survey exist literature survey addressing hardware implementation different tracker missing believe survey would fill gap complete picture existing survey design efficient tracker point future direction researcher follow field highlight lack hardware implementation stateoftheart tracking algorithm well enhanced classical algorithm also stress need measuring tracking performance hardwarebased tracker Additionally enough detail hardwarebased tracker need provided allow reasonable comparison different implementation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Shan Luo', 'Joao Bimbo', 'Ravinder Dahiya', 'Hongbin Liu']

1711.03810v1

Touch sensing can help robots understand their sur- rounding environment, and in particular the objects they interact with. To this end, roboticists have, in the last few decades, developed several tactile sensing solutions, extensively reported in the literature. Research into interpreting the conveyed tactile information has also started to attract increasing attention in recent years. However, a comprehensive study on this topic is yet to be reported. In an effort to collect and summarize the major scientific achievements in the area, this survey extensively reviews current trends in robot tactile perception of object properties. Available tactile sensing technologies are briefly presented before an extensive review on tactile recognition of object properties. The object properties that are targeted by this review are shape, surface material and object pose. The role of touch sensing in combination with other sensing sources is also discussed. In this review, open issues are identified and future directions for applying tactile sensing in different tasks are suggested.

Robotic Tactile Perception of Object Properties: A Review

http://arxiv.org/pdf/1711.03810v1

Title Robotic Tactile Perception Object Properties Review Summary Touch sensing help robot understand sur rounding environment particular object interact end roboticists last decade developed several tactile sensing solution extensively reported literature Research interpreting conveyed tactile information also started attract increasing attention recent year However comprehensive study topic yet reported effort collect summarize major scientific achievement area survey extensively review current trend robot tactile perception object property Available tactile sensing technology briefly presented extensive review tactile recognition object property object property targeted review shape surface material object pose role touch sensing combination sensing source also discussed review open issue identified future direction applying tactile sensing different task suggested Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Redouane Lguensat', 'Miao Sun', 'Ronan Fablet', 'Evan Mason', 'Pierre Tandeo', 'Ge Chen']

1711.03954v1

This work presents EddyNet, a deep learning based architecture for automated eddy detection and classification from Sea Surface Height (SSH) maps provided by the Copernicus Marine and Environment Monitoring Service (CMEMS). EddyNet is a U-Net like network that consists of a convolutional encoder-decoder followed by a pixel-wise classification layer. The output is a map with the same size of the input where pixels have the following labels \{'0': Non eddy, '1': anticyclonic eddy, '2': cyclonic eddy\}. We investigate the use of SELU activation function instead of the classical ReLU+BN and we use an overlap based loss function instead of the cross entropy loss. Keras Python code, the training datasets and EddyNet weights files are open-source and freely available on https://github.com/redouanelg/EddyNet.

EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies

http://arxiv.org/pdf/1711.03954v1

Title EddyNet Deep Neural Network PixelWise Classification Oceanic Eddies Summary work present EddyNet deep learning based architecture automated eddy detection classification Sea Surface Height SSH map provided Copernicus Marine Environment Monitoring Service CMEMS EddyNet UNet like network consists convolutional encoderdecoder followed pixelwise classification layer output map size input pixel following label 0 Non eddy 1 anticyclonic eddy 2 cyclonic eddy investigate use SELU activation function instead classical ReLUBN use overlap based loss function instead cross entropy loss Keras Python code training datasets EddyNet weight file opensource freely available httpsgithubcomredouanelgEddyNet Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Christian S. Perone']

1711.04069v1

In this work, we propose a straightforward method to derive Elliptic Curve Digital Signature Algorithm (ECDSA) key pairs from embeddings created using Deep Learning and Metric Learning approaches. We also show that these keys allows the derivation of cryptocurrencies (such as Bitcoin) addresses that can be used to transfer and receive funds, allowing novel Blockchain-based applications that can be used to transfer funds or data directly to domains such as image, text, sound or any other domain where Deep Learning can extract high-quality embeddings; providing thus a novel integration between the properties of the Blockchain-based technologies such as trust minimization and decentralization together with the high-quality learned representations from Deep Learning techniques.

Towards ECDSA key derivation from deep embeddings for novel Blockchain applications

http://arxiv.org/pdf/1711.04069v1

Title Towards ECDSA key derivation deep embeddings novel Blockchain application Summary work propose straightforward method derive Elliptic Curve Digital Signature Algorithm ECDSA key pair embeddings created using Deep Learning Metric Learning approach also show key allows derivation cryptocurrencies Bitcoin address used transfer receive fund allowing novel Blockchainbased application used transfer fund data directly domain image text sound domain Deep Learning extract highquality embeddings providing thus novel integration property Blockchainbased technology trust minimization decentralization together highquality learned representation Deep Learning technique Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jan Egger', 'Christopher Nimsky', 'Xiaojun Chen']

1711.04592v1

In this contribution, we used the GrowCut segmentation algorithm publicly available in three-dimensional Slicer for three-dimensional segmentation of vertebral bodies. To the best of our knowledge, this is the first time that the GrowCut method has been studied for the usage of vertebral body segmentation. In brief, we found that the GrowCut segmentation times were consistently less than the manual segmentation times. Hence, GrowCut provides an alternative to a manual slice-by-slice segmentation process.

Vertebral body segmentation with GrowCut: Initial experience, workflow and practical application

http://arxiv.org/pdf/1711.04592v1

Title Vertebral body segmentation GrowCut Initial experience workflow practical application Summary contribution used GrowCut segmentation algorithm publicly available threedimensional Slicer threedimensional segmentation vertebral body best knowledge first time GrowCut method studied usage vertebral body segmentation brief found GrowCut segmentation time consistently le manual segmentation time Hence GrowCut provides alternative manual slicebyslice segmentation 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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['Gisele H. B. Miranda', 'Jeaneth Machicao', 'Odemir M. Bruno']

1711.05104v1

The structural analysis of shape boundaries leads to the characterization of objects as well as to the understanding of shape properties. The literature on graphs and networks have contributed to the structural characterization of shapes with different theoretical approaches. We performed a study on the relationship between the shape architecture and the network topology constructed over the shape boundary. For that, we used a method for network modeling proposed in 2009. Firstly, together with curvature analysis, we evaluated the proposed approach for regular polygons. This way, it was possible to investigate how the network measurements vary according to some specific shape properties. Secondly, we evaluated the performance of the proposed shape descriptor in classification tasks for three datasets, accounting for both real-world and synthetic shapes. We demonstrated that not only degree related measurements are capable of distinguishing classes of objects. Yet, when using measurements that account for distinct properties of the network structure, the construction of the shape descriptor becomes more computationally efficient. Given the fact the network is dynamically constructed, the number of iterations can be reduced. The proposed approach accounts for a more robust set of structural measurements, that improved the discriminant power of the shape descriptors.

An optimized shape descriptor based on structural properties of networks

http://arxiv.org/pdf/1711.05104v1

Title optimized shape descriptor based structural property network Summary structural analysis shape boundary lead characterization object well understanding shape property literature graph network contributed structural characterization shape different theoretical approach performed study relationship shape architecture network topology constructed shape boundary used method network modeling proposed 2009 Firstly together curvature analysis evaluated proposed approach regular polygon way possible investigate network measurement vary according specific shape property Secondly evaluated performance proposed shape descriptor classification task three datasets accounting realworld synthetic shape demonstrated degree related measurement capable distinguishing class object Yet using measurement account distinct property network structure construction shape descriptor becomes computationally efficient Given fact network dynamically constructed number iteration reduced proposed approach account robust set structural measurement improved discriminant power shape descriptor Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Nuri Murat Arar', 'Jean-Philippe Thiran']

1711.05444v2

Despite significant advances in improving the gaze tracking accuracy under controlled conditions, the tracking robustness under real-world conditions, such as large head pose and movements, use of eyeglasses, illumination and eye type variations, remains a major challenge in eye tracking. In this paper, we revisit this challenge and introduce a real-time multi-camera eye tracking framework to improve the tracking robustness. First, differently from previous work, we design a multi-view tracking setup that allows for acquiring multiple eye appearances simultaneously. Leveraging multi-view appearances enables to more reliably detect gaze features under challenging conditions, particularly when they are obstructed in conventional single-view appearance due to large head movements or eyewear effects. The features extracted on various appearances are then used for estimating multiple gaze outputs. Second, we propose to combine estimated gaze outputs through an adaptive fusion mechanism to compute user's overall point of regard. The proposed mechanism firstly determines the estimation reliability of each gaze output according to user's momentary head pose and predicted gazing behavior, and then performs a reliability-based weighted fusion. We demonstrate the efficacy of our framework with extensive simulations and user experiments on a collected dataset featuring 20 subjects. Our results show that in comparison with state-of-the-art eye trackers, the proposed framework provides not only a significant enhancement in accuracy but also a notable robustness. Our prototype system runs at 30 frames-per-second (fps) and achieves 1 degree accuracy under challenging experimental scenarios, which makes it suitable for applications demanding high accuracy and robustness.

Robust Real-Time Multi-View Eye Tracking

http://arxiv.org/pdf/1711.05444v2

Title Robust RealTime MultiView Eye Tracking Summary Despite significant advance improving gaze tracking accuracy controlled condition tracking robustness realworld condition large head pose movement use eyeglass illumination eye type variation remains major challenge eye tracking paper revisit challenge introduce realtime multicamera eye tracking framework improve tracking robustness First differently previous work design multiview tracking setup allows acquiring multiple eye appearance simultaneously Leveraging multiview appearance enables reliably detect gaze feature challenging condition particularly obstructed conventional singleview appearance due large head movement eyewear effect feature extracted various appearance used estimating multiple gaze output Second propose combine estimated gaze output adaptive fusion mechanism compute user overall point regard proposed mechanism firstly determines estimation reliability gaze output according user momentary head pose predicted gazing behavior performs reliabilitybased weighted fusion demonstrate efficacy framework extensive simulation user experiment collected dataset featuring 20 subject result show comparison stateoftheart eye tracker proposed framework provides significant enhancement accuracy also notable robustness prototype system run 30 framespersecond fps achieves 1 degree accuracy challenging experimental scenario make suitable application demanding high accuracy robustness Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Appina Balasubramanyam', 'Jalli Akshith', 'Battula Shanmukh Srinivas', 'Channappayya S Sumohana']

1711.05480v1

We present a no reference (NR) quality assessment algorithm for assessing the perceptual quality of natural stereoscopic 3D (S3D) videos. This work is inspired by our finding that the joint statistics of the subband coefficients of motion (optical flow or motion vector magnitude) and depth (disparity map) of natural S3D videos possess a unique signature. Specifically, we empirically show that the joint statistics of the motion and depth subband coefficients of S3D video frames can be modeled accurately using a Bivariate Generalized Gaussian Distribution (BGGD). We then demonstrate that the parameters of the BGGD model possess the ability to discern quality variations in S3D videos. Therefore, the BGGD model parameters are employed as motion and depth quality features. In addition to these features, we rely on a frame level spatial quality feature that is computed using a robust off the shelf NR image quality assessment (IQA) algorithm. These frame level motion, depth and spatial features are consolidated and used with the corresponding S3D video's difference mean opinion score (DMOS) labels for supervised learning using support vector regression (SVR). The overall quality of an S3D video is computed by averaging the frame level quality predictions of the constituent video frames. The proposed algorithm, dubbed Video QUality Evaluation using MOtion and DEpth Statistics (VQUEMODES) is shown to outperform the state of the art methods when evaluated over the IRCCYN and LFOVIA S3D subjective quality assessment databases.

No Reference Stereoscopic Video Quality Assessment Using Joint Motion and Depth Statistics

http://arxiv.org/pdf/1711.05480v1

Title Reference Stereoscopic Video Quality Assessment Using Joint Motion Depth Statistics Summary present reference NR quality assessment algorithm assessing perceptual quality natural stereoscopic 3D S3D video work inspired finding joint statistic subband coefficient motion optical flow motion vector magnitude depth disparity map natural S3D video posse unique signature Specifically empirically show joint statistic motion depth subband coefficient S3D video frame modeled accurately using Bivariate Generalized Gaussian Distribution BGGD demonstrate parameter BGGD model posse ability discern quality variation S3D video Therefore BGGD model parameter employed motion depth quality feature addition feature rely frame level spatial quality feature computed using robust shelf NR image quality assessment IQA algorithm frame level motion depth spatial feature consolidated used corresponding S3D video difference mean opinion score DMOS label supervised learning using support vector regression SVR overall quality S3D video computed averaging frame level quality prediction constituent video frame proposed algorithm dubbed Video QUality Evaluation using MOtion DEpth Statistics VQUEMODES shown outperform state art method evaluated IRCCYN LFOVIA S3D subjective quality assessment database Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Zhedong Zheng', 'Liang Zheng', 'Michael Garrett', 'Yi Yang', 'Yi-Dong Shen']

1711.05535v2

This paper considers the task of matching images and sentences. The challenge consists in discriminatively embedding the two modalities onto a shared visual-textual space. Existing work in this field largely uses Recurrent Neural Networks (RNN) for text feature learning and employs off-the-shelf Convolutional Neural Networks (CNN) for image feature extraction. Our system, in comparison, differs in two key aspects. Firstly, we build a convolutional network amenable for fine-tuning the visual and textual representations, where the entire network only contains four components, i.e., convolution layer, pooling layer, rectified linear unit function (ReLU), and batch normalisation. End-to-end learning allows the system to directly learn from the data and fully utilise the supervisions. Secondly, we propose instance loss according to viewing each multimodal data pair as a class. This works with a large margin objective to learn the inter-modal correspondence between images and their textual descriptions. Experiments on two generic retrieval datasets (Flickr30k and MSCOCO) demonstrate that our method yields competitive accuracy compared to state-of-the-art methods. Moreover, in language person retrieval, we improve the state of the art by a large margin. Code is available at https://github. com/layumi/Image-Text-Embedding

Dual-Path Convolutional Image-Text Embedding

http://arxiv.org/pdf/1711.05535v2

Title DualPath Convolutional ImageText Embedding Summary paper considers task matching image sentence challenge consists discriminatively embedding two modality onto shared visualtextual space Existing work field largely us Recurrent Neural Networks RNN text feature learning employ offtheshelf Convolutional Neural Networks CNN image feature extraction system comparison differs two key aspect Firstly build convolutional network amenable finetuning visual textual representation entire network contains four component ie convolution layer pooling layer rectified linear unit function ReLU batch normalisation Endtoend learning allows system directly learn data fully utilise supervision Secondly propose instance loss according viewing multimodal data pair class work large margin objective learn intermodal correspondence image textual description Experiments two generic retrieval datasets Flickr30k MSCOCO demonstrate method yield competitive accuracy compared stateoftheart method Moreover language person retrieval improve state art large margin Code available httpsgithub comlayumiImageTextEmbedding Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Guowei Wan', 'Xiaolong Yang', 'Renlan Cai', 'Hao Li', 'Hao Wang', 'Shiyu Song']

1711.05805v1

We present a robust and precise localization system that achieves centimeter-level localization accuracy in disparate city scenes. Our system adaptively uses information from complementary sensors such as GNSS, LiDAR, and IMU to achieve high localization accuracy and resilience in challenging scenes, such as urban downtown, highways, and tunnels. Rather than relying only on LiDAR intensity or 3D geometry, we make innovative use of LiDAR intensity and altitude cues to significantly improve localization system accuracy and robustness. Our GNSS RTK module utilizes the help of the multi-sensor fusion framework and achieves a better ambiguity resolution success rate. An error-state Kalman filter is applied to fuse the localization measurements from different sources with novel uncertainty estimation. We validate, in detail, the effectiveness of our approaches, achieving 5-10cm RMS accuracy and outperforming previous state-of-the-art systems. Importantly, our system, while deployed in a large autonomous driving fleet, made our vehicles fully autonomous in crowded city streets despite road construction that occurred from time to time. A dataset including more than 60 km real traffic driving in various urban roads is used to comprehensively test our system.

Robust and Precise Vehicle Localization based on Multi-sensor Fusion in Diverse City Scenes

http://arxiv.org/pdf/1711.05805v1

Title Robust Precise Vehicle Localization based Multisensor Fusion Diverse City Scenes Summary present robust precise localization system achieves centimeterlevel localization accuracy disparate city scene system adaptively us information complementary sensor GNSS LiDAR IMU achieve high localization accuracy resilience challenging scene urban downtown highway tunnel Rather relying LiDAR intensity 3D geometry make innovative use LiDAR intensity altitude cue significantly improve localization system accuracy robustness GNSS RTK module utilizes help multisensor fusion framework achieves better ambiguity resolution success rate errorstate Kalman filter applied fuse localization measurement different source novel uncertainty estimation validate detail effectiveness approach achieving 510cm RMS accuracy outperforming previous stateoftheart system Importantly system deployed large autonomous driving fleet made vehicle fully autonomous crowded city street despite road construction occurred time time dataset including 60 km real traffic driving various urban road used comprehensively test 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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['John Stechschulte', 'Christoffer Heckman']

1711.05864v1

When registering point clouds resolved from an underlying 2-D pixel structure, such as those resulting from structured light and flash LiDAR sensors, or stereo reconstruction, it is expected that some points in one cloud do not have corresponding points in the other cloud, and that these would occur together, such as along an edge of the depth map. In this work, a hidden Markov random field model is used to capture this prior within the framework of the iterative closest point algorithm. The EM algorithm is used to estimate the distribution parameters and the hidden component memberships. Experiments are presented demonstrating that this method outperforms several other outlier rejection methods when the point clouds have low or moderate overlap.

Hidden Markov Random Field Iterative Closest Point

http://arxiv.org/pdf/1711.05864v1

Title Hidden Markov Random Field Iterative Closest Point Summary registering point cloud resolved underlying 2D pixel structure resulting structured light flash LiDAR sensor stereo reconstruction expected point one cloud corresponding point cloud would occur together along edge depth map work hidden Markov random field model used capture prior within framework iterative closest point algorithm EM algorithm used estimate distribution parameter hidden component membership Experiments presented demonstrating method outperforms several outlier rejection method point cloud low moderate overlap Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['He Zhang', 'Hanlin Mo', 'You Hao', 'Shirui Li', 'Hua Li']

1711.05866v2

Chirality plays an important role in physics, chemistry, biology, and other fields. It describes an essential symmetry in structure. However, chirality invariants are usually complicated in expression or difficult to evaluate. In this paper, we present five general three-dimensional chirality invariants based on the generating functions. And the five chiral invariants have four characteristics:(1) They play an important role in the detection of symmetry, especially in the treatment of 'false zero' problem. (2) Three of the five chiral invariants decode an universal chirality index. (3) Three of them are proposed for the first time. (4) The five chiral invariants have low order no bigger than 4, brief expression, low time complexity O(n) and can act as descriptors of three-dimensional objects in shape analysis. The five chiral invariants give a geometric view to study the chiral invariants. And the experiments show that the five chirality invariants are effective and efficient, they can be used as a tool for symmetry detection or features in shape analysis.

Fast and Efficient Calculations of Structural Invariants of Chirality

http://arxiv.org/pdf/1711.05866v2

Title Fast Efficient Calculations Structural Invariants Chirality Summary Chirality play important role physic chemistry biology field describes essential symmetry structure However chirality invariant usually complicated expression difficult evaluate paper present five general threedimensional chirality invariant based generating function five chiral invariant four characteristics1 play important role detection symmetry especially treatment false zero problem 2 Three five chiral invariant decode universal chirality index 3 Three proposed first time 4 five chiral invariant low order bigger 4 brief expression low time complexity act descriptor threedimensional object shape analysis five chiral invariant give geometric view study chiral invariant experiment show five chirality invariant effective efficient used tool symmetry detection feature shape 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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['Rüdiger Göbl', 'Nassir Navab', 'Christoph Hennersperger']

1711.06127v2

Research in ultrasound imaging is limited in reproducibility by two factors: First, many existing ultrasound pipelines are protected by intellectual property, rendering exchange of code difficult. Second, most pipelines are implemented in special hardware, resulting in limited flexibility of implemented processing steps on such platforms. Methods: With SUPRA we propose an open-source pipeline for fully Software Defined Ultrasound Processing for Real-time Applications to alleviate these problems. Covering all steps from beamforming to output of B-mode images, SUPRA can help improve the reproducibility of results and make modifications to the image acquisition mode accessible to the research community. We evaluate the pipeline qualitatively, quantitatively, and regarding its run-time. Results: The pipeline shows image quality comparable to a clinical system and backed by point-spread function measurements a comparable resolution. Including all processing stages of a usual ultrasound pipeline, the run-time analysis shows that it can be executed in 2D and 3D on consumer GPUs in real-time. Conclusions: Our software ultrasound pipeline opens up the research in image acquisition. Given access to ultrasound data from early stages (raw channel data, radiofrequency data) it simplifies the development in imaging. Furthermore, it tackles the reproducibility of research results, as code can be shared easily and even be executed without dedicated ultrasound hardware.

SUPRA: Open Source Software Defined Ultrasound Processing for Real-Time Applications

http://arxiv.org/pdf/1711.06127v2

Title SUPRA Open Source Software Defined Ultrasound Processing RealTime Applications Summary Research ultrasound imaging limited reproducibility two factor First many existing ultrasound pipeline protected intellectual property rendering exchange code difficult Second pipeline implemented special hardware resulting limited flexibility implemented processing step platform Methods SUPRA propose opensource pipeline fully Software Defined Ultrasound Processing Realtime Applications alleviate problem Covering step beamforming output Bmode image SUPRA help improve reproducibility result make modification image acquisition mode accessible research community evaluate pipeline qualitatively quantitatively regarding runtime Results pipeline show image quality comparable clinical system backed pointspread function measurement comparable resolution Including processing stage usual ultrasound pipeline runtime analysis show executed 2D 3D consumer GPUs realtime Conclusions software ultrasound pipeline open research image acquisition Given access ultrasound data early stage raw channel data radiofrequency data simplifies development imaging Furthermore tackle reproducibility research result code shared easily even executed without dedicated ultrasound hardware Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Atique ur Rehman', 'Rafia Rahim', 'M Shahroz Nadeem', 'Sibt ul Hussain']

1711.07201v1

All the existing image steganography methods use manually crafted features to hide binary payloads into cover images. This leads to small payload capacity and image distortion. Here we propose a convolutional neural network based encoder-decoder architecture for embedding of images as payload. To this end, we make following three major contributions: (i) we propose a deep learning based generic encoder-decoder architecture for image steganography; (ii) we introduce a new loss function that ensures joint end-to-end training of encoder-decoder networks; (iii) we perform extensive empirical evaluation of proposed architecture on a range of challenging publicly available datasets (MNIST, CIFAR10, PASCAL-VOC12, ImageNet, LFW) and report state-of-the-art payload capacity at high PSNR and SSIM values.

End-to-end Trained CNN Encode-Decoder Networks for Image Steganography

http://arxiv.org/pdf/1711.07201v1

Title Endtoend Trained CNN EncodeDecoder Networks Image Steganography Summary existing image steganography method use manually crafted feature hide binary payload cover image lead small payload capacity image distortion propose convolutional neural network based encoderdecoder architecture embedding image payload end make following three major contribution propose deep learning based generic encoderdecoder architecture image steganography ii introduce new loss function ensures joint endtoend training encoderdecoder network iii perform extensive empirical evaluation proposed architecture range challenging publicly available datasets MNIST CIFAR10 PASCALVOC12 ImageNet LFW report stateoftheart payload capacity high PSNR SSIM value Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Weiyao Lin', 'Yang Mi', 'Jianxin Wu', 'Ke Lu', 'Hongkai Xiong']

1711.07430v1

Action recognition is an important yet challenging task in computer vision. In this paper, we propose a novel deep-based framework for action recognition, which improves the recognition accuracy by: 1) deriving more precise features for representing actions, and 2) reducing the asynchrony between different information streams. We first introduce a coarse-to-fine network which extracts shared deep features at different action class granularities and progressively integrates them to obtain a more accurate feature representation for input actions. We further introduce an asynchronous fusion network. It fuses information from different streams by asynchronously integrating stream-wise features at different time points, hence better leveraging the complementary information in different streams. Experimental results on action recognition benchmarks demonstrate that our approach achieves the state-of-the-art performance.

Action Recognition with Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion

http://arxiv.org/pdf/1711.07430v1

Title Action Recognition CoarsetoFine Deep Feature Integration Asynchronous Fusion Summary Action recognition important yet challenging task computer vision paper propose novel deepbased framework action recognition improves recognition accuracy 1 deriving precise feature representing action 2 reducing asynchrony different information stream first introduce coarsetofine network extract shared deep feature different action class granularity progressively integrates obtain accurate feature representation input action introduce asynchronous fusion network fuse information different stream asynchronously integrating streamwise feature different time point hence better leveraging complementary information different stream Experimental result action recognition benchmark demonstrate approach achieves stateoftheart performance Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Xingxing Zuo', 'Xiaojia Xie', 'Yong Liu', 'Guoquan Huang']

1711.08654v1

In this paper, we develop a robust efficient visual SLAM system that utilizes heterogeneous point and line features. By leveraging ORB-SLAM [1], the proposed system consists of stereo matching, frame tracking, local mapping, loop detection, and bundle adjustment of both point and line features. In particular, as the main theoretical contributions of this paper, we, for the first time, employ the orthonormal representation as the minimal parameterization to model line features along with point features in visual SLAM and analytically derive the Jacobians of the re-projection errors with respect to the line parameters, which significantly improves the SLAM solution. The proposed SLAM has been extensively tested in both synthetic and real-world experiments whose results demonstrate that the proposed system outperforms the state-of-the-art methods in various scenarios.

Robust Visual SLAM with Point and Line Features

http://arxiv.org/pdf/1711.08654v1

Title Robust Visual SLAM Point Line Features Summary paper develop robust efficient visual SLAM system utilizes heterogeneous point line feature leveraging ORBSLAM 1 proposed system consists stereo matching frame tracking local mapping loop detection bundle adjustment point line feature particular main theoretical contribution paper first time employ orthonormal representation minimal parameterization model line feature along point feature visual SLAM analytically derive Jacobians reprojection error respect line parameter significantly improves SLAM solution proposed SLAM extensively tested synthetic realworld experiment whose result demonstrate proposed system outperforms stateoftheart method various 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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['Wenjie Pei', 'Hamdi Dibeklioğlu', 'Tadas Baltrušaitis', 'David M. J. Tax']

1711.08690v1

The main challenges of age estimation from facial expression videos lie not only in the modeling of the static facial appearance, but also in the capturing of the temporal facial dynamics. Traditional techniques to this problem focus on constructing handcrafted features to explore the discriminative information contained in facial appearance and dynamics separately. This relies on sophisticated feature-refinement and framework-design. In this paper, we present an end-to-end architecture for age estimation which is able to simultaneously learn both the appearance and dynamics of age from raw videos of facial expressions. Specifically, we employ convolutional neural networks to extract effective latent appearance representations and feed them into recurrent networks to model the temporal dynamics. More importantly, we propose to leverage attention models for salience detection in both the spatial domain for each single image and the temporal domain for the whole video as well. We design a specific spatially-indexed attention mechanism among the convolutional layers to extract the salient facial regions in each individual image, and a temporal attention layer to assign attention weights to each frame. This two-pronged approach not only improves the performance by allowing the model to focus on informative frames and facial areas, but it also offers an interpretable correspondence between the spatial facial regions as well as temporal frames, and the task of age estimation. We demonstrate the strong performance of our model in experiments on a large, gender-balanced database with 400 subjects with ages spanning from 8 to 76 years. Experiments reveal that our model exhibits significant superiority over the state-of-the-art methods given sufficient training data.

Attended End-to-end Architecture for Age Estimation from Facial Expression Videos

http://arxiv.org/pdf/1711.08690v1

Title Attended Endtoend Architecture Age Estimation Facial Expression Videos Summary main challenge age estimation facial expression video lie modeling static facial appearance also capturing temporal facial dynamic Traditional technique problem focus constructing handcrafted feature explore discriminative information contained facial appearance dynamic separately relies sophisticated featurerefinement frameworkdesign paper present endtoend architecture age estimation able simultaneously learn appearance dynamic age raw video facial expression Specifically employ convolutional neural network extract effective latent appearance representation feed recurrent network model temporal dynamic importantly propose leverage attention model salience detection spatial domain single image temporal domain whole video well design specific spatiallyindexed attention mechanism among convolutional layer extract salient facial region individual image temporal attention layer assign attention weight frame twopronged approach improves performance allowing model focus informative frame facial area also offer interpretable correspondence spatial facial region well temporal frame task age estimation demonstrate strong performance model experiment large genderbalanced database 400 subject age spanning 8 76 year Experiments reveal model exhibit significant superiority stateoftheart method given sufficient training data Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Shangzhe Wu', 'Jiarui Xu', 'Yu-Wing Tai', 'Chi-Keung Tang']

1711.08937v1

This paper proposes the first end-to-end deep framework for high dynamic range (HDR) imaging of dynamic scenes with large-scale foreground motions. In state-of-the-art deep HDR imaging such as [13], the problem is formulated as an image composition problem, by first aligning input images using optical flows which are still error-prone due to occlusion and large motions. In our end-to-end approach, HDR imaging is formulated as an image translation problem and no optical flows are used. Moreover, our simple translation network can automatically hallucinate plausible HDR details in the presence of total occlusion, saturation and under-exposure, which are otherwise almost impossible to recover by conventional optimization approaches. We perform extensive qualitative and quantitative comparisons to show that our end-to-end HDR approach produces excellent results where color artifacts and geometry distortion are significantly reduced compared with existing state-ofthe-art methods.

End-to-End Deep HDR Imaging with Large Foreground Motions

http://arxiv.org/pdf/1711.08937v1

Title EndtoEnd Deep HDR Imaging Large Foreground Motions Summary paper proposes first endtoend deep framework high dynamic range HDR imaging dynamic scene largescale foreground motion stateoftheart deep HDR imaging 13 problem formulated image composition problem first aligning input image using optical flow still errorprone due occlusion large motion endtoend approach HDR imaging formulated image translation problem optical flow used Moreover simple translation network automatically hallucinate plausible HDR detail presence total occlusion saturation underexposure otherwise almost impossible recover conventional optimization approach perform extensive qualitative quantitative comparison show endtoend HDR approach produce excellent result color artifact geometry distortion significantly reduced compared existing 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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['Sherif Abdulatif', 'Fady Aziz', 'Bernhard Kleiner', 'Urs Schneider']

1711.09175v1

Unique micro-Doppler signature ($\boldsymbol{\mu}$-D) of a human body motion can be analyzed as the superposition of different body parts $\boldsymbol{\mu}$-D signatures. Extraction of human limbs $\boldsymbol{\mu}$-D signatures in real-time can be used to detect, classify and track human motion especially for safety application. In this paper, two methods are combined to simulate $\boldsymbol{\mu}$-D signatures of a walking human. Furthermore, a novel limbs $\mu$-D signature time independent decomposition feasibility study is presented based on features as $\mu$-D signatures and range profiles also known as micro-Range ($\mu$-R). Walking human body parts can be divided into four classes (base, arms, legs, feet) and a decision tree classifier is used. Validation is done and the classifier is able to decompose $\mu$-D signatures of limbs from a walking human signature on real-time basis.

Real-Time Capable Micro-Doppler Signature Decomposition of Walking Human Limbs

http://arxiv.org/pdf/1711.09175v1

Title RealTime Capable MicroDoppler Signature Decomposition Walking Human Limbs Summary Unique microDoppler signature boldsymbolmuD human body motion analyzed superposition different body part boldsymbolmuD signature Extraction human limb boldsymbolmuD signature realtime used detect classify track human motion especially safety application paper two method combined simulate boldsymbolmuD signature walking human Furthermore novel limb muD signature time independent decomposition feasibility study presented based feature muD signature range profile also known microRange muR Walking human body part divided four class base arm leg foot decision tree classifier used Validation done classifier able decompose muD signature limb walking human signature realtime basis Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sherif Abdulatif', 'Qian Wei', 'Fady Aziz', 'Bernhard Kleiner', 'Urs Schneider']

1711.09177v3

Radar sensors can be used for analyzing the induced frequency shifts due to micro-motions in both range and velocity dimensions identified as micro-Doppler ($\boldsymbol{\mu}$-D) and micro-Range ($\boldsymbol{\mu}$-R), respectively. Different moving targets will have unique $\boldsymbol{\mu}$-D and $\boldsymbol{\mu}$-R signatures that can be used for target classification. Such classification can be used in numerous fields, such as gait recognition, safety and surveillance. In this paper, a 25 GHz FMCW Single-Input Single-Output (SISO) radar is used in industrial safety for real-time human-robot identification. Due to the real-time constraint, joint Range-Doppler (R-D) maps are directly analyzed for our classification problem. Furthermore, a comparison between the conventional classical learning approaches with handcrafted extracted features, ensemble classifiers and deep learning approaches is presented. For ensemble classifiers, restructured range and velocity profiles are passed directly to ensemble trees, such as gradient boosting and random forest without feature extraction. Finally, a Deep Convolutional Neural Network (DCNN) is used and raw R-D images are directly fed into the constructed network. DCNN shows a superior performance of 99\% accuracy in identifying humans from robots on a single R-D map.

Micro-Doppler Based Human-Robot Classification Using Ensemble and Deep Learning Approaches

http://arxiv.org/pdf/1711.09177v3

Title MicroDoppler Based HumanRobot Classification Using Ensemble Deep Learning Approaches Summary Radar sensor used analyzing induced frequency shift due micromotions range velocity dimension identified microDoppler boldsymbolmuD microRange boldsymbolmuR respectively Different moving target unique boldsymbolmuD boldsymbolmuR signature used target classification classification used numerous field gait recognition safety surveillance paper 25 GHz FMCW SingleInput SingleOutput SISO radar used industrial safety realtime humanrobot identification Due realtime constraint joint RangeDoppler RD map directly analyzed classification problem Furthermore comparison conventional classical learning approach handcrafted extracted feature ensemble classifier deep learning approach presented ensemble classifier restructured range velocity profile passed directly ensemble tree gradient boosting random forest without feature extraction Finally Deep Convolutional Neural Network DCNN used raw RD image directly fed constructed network DCNN show superior performance 99 accuracy identifying human robot single RD map Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Khanh Quoc Dinh', 'Thuong Nguyen Canh', 'Byeungwoo Jeon']

1711.09375v1

This paper addresses an ill-posed problem of recovering a color image from its compressively sensed measurement data. Differently from the typical 1D vector-based approach of the state-of-the-art methods, we exploit the nonlocal similarities inherently existing in images by treating each patch of a color image as a 3D tensor consisting of not only horizontal and vertical but also spectral dimensions. A group of nonlocal similar patches form a 4D tensor for which a nonlocal higher order dictionary is learned via higher order singular value decomposition. The multiple sub-dictionaries contained in the higher order dictionary decorrelate the group in each corresponding dimension, thus help the detail of color images to be reconstructed better. Furthermore, we promote sparsity of the final solution using a sparsity regularization based on a weight tensor. It can distinguish those coefficients of the sparse representation generated by the higher order dictionary which are expected to have large magnitude from the others in the optimization. Accordingly, in the iterative solution, it acts like a weighting process which is designed by approximating the minimum mean squared error filter for more faithful recovery. Experimental results confirm improvement by the proposed method over the state-of-the-art ones.

Compressive Sensing of Color Images Using Nonlocal Higher Order Dictionary

http://arxiv.org/pdf/1711.09375v1

Title Compressive Sensing Color Images Using Nonlocal Higher Order Dictionary Summary paper address illposed problem recovering color image compressively sensed measurement data Differently typical 1D vectorbased approach stateoftheart method exploit nonlocal similarity inherently existing image treating patch color image 3D tensor consisting horizontal vertical also spectral dimension group nonlocal similar patch form 4D tensor nonlocal higher order dictionary learned via higher order singular value decomposition multiple subdictionaries contained higher order dictionary decorrelate group corresponding dimension thus help detail color image reconstructed better Furthermore promote sparsity final solution using sparsity regularization based weight tensor distinguish coefficient sparse representation generated higher order dictionary expected large magnitude others optimization Accordingly iterative solution act like weighting process designed approximating minimum mean squared error filter faithful recovery Experimental result confirm improvement proposed method stateoftheart one Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Anthony Yezzi', 'Ganesh Sundaramoorthi']

1711.09867v1

Following the seminal work of Nesterov, accelerated optimization methods have been used to powerfully boost the performance of first-order, gradient-based parameter estimation in scenarios where second-order optimization strategies are either inapplicable or impractical. Not only does accelerated gradient descent converge considerably faster than traditional gradient descent, but it also performs a more robust local search of the parameter space by initially overshooting and then oscillating back as it settles into a final configuration, thereby selecting only local minimizers with a basis of attraction large enough to contain the initial overshoot. This behavior has made accelerated and stochastic gradient search methods particularly popular within the machine learning community. In their recent PNAS 2016 paper, Wibisono, Wilson, and Jordan demonstrate how a broad class of accelerated schemes can be cast in a variational framework formulated around the Bregman divergence, leading to continuum limit ODE's. We show how their formulation may be further extended to infinite dimension manifolds (starting here with the geometric space of curves and surfaces) by substituting the Bregman divergence with inner products on the tangent space and explicitly introducing a distributed mass model which evolves in conjunction with the object of interest during the optimization process. The co-evolving mass model, which is introduced purely for the sake of endowing the optimization with helpful dynamics, also links the resulting class of accelerated PDE based optimization schemes to fluid dynamical formulations of optimal mass transport.

Accelerated Optimization in the PDE Framework: Formulations for the Active Contour Case

http://arxiv.org/pdf/1711.09867v1

Title Accelerated Optimization PDE Framework Formulations Active Contour Case Summary Following seminal work Nesterov accelerated optimization method used powerfully boost performance firstorder gradientbased parameter estimation scenario secondorder optimization strategy either inapplicable impractical accelerated gradient descent converge considerably faster traditional gradient descent also performs robust local search parameter space initially overshooting oscillating back settle final configuration thereby selecting local minimizers basis attraction large enough contain initial overshoot behavior made accelerated stochastic gradient search method particularly popular within machine learning community recent PNAS 2016 paper Wibisono Wilson Jordan demonstrate broad class accelerated scheme cast variational framework formulated around Bregman divergence leading continuum limit ODEs show formulation may extended infinite dimension manifold starting geometric space curve surface substituting Bregman divergence inner product tangent space explicitly introducing distributed mass model evolves conjunction object interest optimization process coevolving mass model introduced purely sake endowing optimization helpful dynamic also link resulting class accelerated PDE based optimization scheme fluid dynamical formulation optimal mass transport Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ping Guo', 'Fuqing Duan', 'Pei Wang', 'Yao Yao', 'Xin Xin']

1711.10339v1

Discovering pulsars is a significant and meaningful research topic in the field of radio astronomy. With the advent of astronomical instruments such as he Five-hundred-meter Aperture Spherical Telescope (FAST) in China, data volumes and data rates are exponentially growing. This fact necessitates a focus on artificial intelligence (AI) technologies that can perform the automatic pulsar candidate identification to mine large astronomical data sets. Automatic pulsar candidate identification can be considered as a task of determining potential candidates for further investigation and eliminating noises of radio frequency interferences or other non-pulsar signals. It is very hard to raise the performance of DCNN-based pulsar identification because the limited training samples restrict network structure to be designed deep enough for learning good features as well as the crucial class imbalance problem due to very limited number of real pulsar samples. To address these problems, we proposed a framework which combines deep convolution generative adversarial network (DCGAN) with support vector machine (SVM) to deal with imbalance class problem and to improve pulsar identification accuracy. DCGAN is used as sample generation and feature learning model, and SVM is adopted as the classifier for predicting candidate's labels in the inference stage. The proposed framework is a novel technique which not only can solve imbalance class problem but also can learn discriminative feature representations of pulsar candidates instead of computing hand-crafted features in preprocessing steps too, which makes it more accurate for automatic pulsar candidate selection. Experiments on two pulsar datasets verify the effectiveness and efficiency of our proposed method.

Pulsar Candidate Identification with Artificial Intelligence Techniques

http://arxiv.org/pdf/1711.10339v1

Title Pulsar Candidate Identification Artificial Intelligence Techniques Summary Discovering pulsar significant meaningful research topic field radio astronomy advent astronomical instrument Fivehundredmeter Aperture Spherical Telescope FAST China data volume data rate exponentially growing fact necessitates focus artificial intelligence AI technology perform automatic pulsar candidate identification mine large astronomical data set Automatic pulsar candidate identification considered task determining potential candidate investigation eliminating noise radio frequency interference nonpulsar signal hard raise performance DCNNbased pulsar identification limited training sample restrict network structure designed deep enough learning good feature well crucial class imbalance problem due limited number real pulsar sample address problem proposed framework combine deep convolution generative adversarial network DCGAN support vector machine SVM deal imbalance class problem improve pulsar identification accuracy DCGAN used sample generation feature learning model SVM adopted classifier predicting candidate label inference stage proposed framework novel technique solve imbalance class problem also learn discriminative feature representation pulsar candidate instead computing handcrafted feature preprocessing step make accurate automatic pulsar candidate selection Experiments two pulsar datasets verify effectiveness efficiency 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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['Ibrahim Abdelkader', 'Yasser El-Sonbaty', 'Mohamed El-Habrouk']

1711.10464v1

Advances in semiconductor manufacturing processes and large scale integration keep pushing demanding applications further away from centralized processing, and closer to the edges of the network (i.e. Edge Computing). It has become possible to perform complex in-network image processing using low-power embedded smart cameras, enabling a multitude of new collaborative image processing applications. This paper introduces OpenMV, a new low-power smart camera that lends itself naturally to wireless sensor networks and machine vision applications. The uniqueness of this platform lies in running an embedded Python3 interpreter, allowing its peripherals and machine vision library to be scripted in Python. In addition, its hardware is extensible via modules that augment the platform with new capabilities, such as thermal imaging and networking modules.

Openmv: A Python powered, extensible machine vision camera

http://arxiv.org/pdf/1711.10464v1

Title Openmv Python powered extensible machine vision camera Summary Advances semiconductor manufacturing process large scale integration keep pushing demanding application away centralized processing closer edge network ie Edge Computing become possible perform complex innetwork image processing using lowpower embedded smart camera enabling multitude new collaborative image processing application paper introduces OpenMV new lowpower smart camera lends naturally wireless sensor network machine vision application uniqueness platform lie running embedded Python3 interpreter allowing peripheral machine vision library scripted Python addition hardware extensible via module augment platform new capability thermal imaging networking module Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['V. Estellers', 'F. R. Schmidt', 'D. Cremers']

1711.10824v1

Most 3D shape analysis methods use triangular meshes to discretize both the shape and functions on it as piecewise linear functions. With this representation, shape analysis requires fine meshes to represent smooth shapes and geometric operators like normals, curvatures, or Laplace-Beltrami eigenfunctions at large computational and memory costs. We avoid this bottleneck with a compression technique that represents a smooth shape as subdivision surfaces and exploits the subdivision scheme to parametrize smooth functions on that shape with a few control parameters. This compression does not affect the accuracy of the Laplace-Beltrami operator and its eigenfunctions and allow us to compute shape descriptors and shape matchings at an accuracy comparable to triangular meshes but a fraction of the computational cost. Our framework can also compress surfaces represented by point clouds to do shape analysis of 3D scanning data.

Compression for Smooth Shape Analysis

http://arxiv.org/pdf/1711.10824v1

Title Compression Smooth Shape Analysis Summary 3D shape analysis method use triangular mesh discretize shape function piecewise linear function representation shape analysis requires fine mesh represent smooth shape geometric operator like normal curvature LaplaceBeltrami eigenfunctions large computational memory cost avoid bottleneck compression technique represents smooth shape subdivision surface exploit subdivision scheme parametrize smooth function shape control parameter compression affect accuracy LaplaceBeltrami operator eigenfunctions allow u compute shape descriptor shape matchings accuracy comparable triangular mesh fraction computational cost framework also compress surface represented point cloud shape analysis 3D scanning data Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Damiana Lazzaro', 'Elena Loli Piccolomini', 'Fabiana Zama']

1711.11075v1

In this paper we present a fast and efficient method for the reconstruction of Magnetic Resonance Images (MRI) from severely under-sampled data. From the Compressed Sensing theory we have mathematically modeled the problem as a constrained minimization problem with a family of non-convex regularizing objective functions depending on a parameter and a least squares data fit constraint. We propose a fast and efficient algorithm, named Fast NonConvex Reweighting (FNCR) algorithm, based on an iterative scheme where the non-convex problem is approximated by its convex linearization and the penalization parameter is automatically updated. The convex problem is solved by a Forward-Backward procedure, where the Backward step is performed by a Split Bregman strategy. Moreover, we propose a new efficient iterative solver for the arising linear systems. We prove the convergence of the proposed FNCR method. The results on synthetic phantoms and real images show that the algorithm is very well performing and computationally efficient, even when compared to the best performing methods proposed in the literature.

A fast nonconvex Compressed Sensing algorithm for highly low-sampled MR images reconstruction

http://arxiv.org/pdf/1711.11075v1

Title fast nonconvex Compressed Sensing algorithm highly lowsampled MR image reconstruction Summary paper present fast efficient method reconstruction Magnetic Resonance Images MRI severely undersampled data Compressed Sensing theory mathematically modeled problem constrained minimization problem family nonconvex regularizing objective function depending parameter least square data fit constraint propose fast efficient algorithm named Fast NonConvex Reweighting FNCR algorithm based iterative scheme nonconvex problem approximated convex linearization penalization parameter automatically updated convex problem solved ForwardBackward procedure Backward step performed Split Bregman strategy Moreover propose new efficient iterative solver arising linear system prove convergence proposed FNCR method result synthetic phantom real image show algorithm well performing computationally efficient even compared best performing method proposed literature Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Song Yizhi', 'Xu Cheng', 'Ding Daoxin', 'Zhou Hang', 'Quan Tingwei', 'Li Shiwei']

1711.11224v1

Convolution system is linear and time invariant, and can describe the optical imaging process. Based on convolution system, many deconvolution techniques have been developed for optical image analysis, such as boosting the space resolution of optical images, image denoising, image enhancement and so on. Here, we gave properties on N-dimensional convolution. By using these properties, we proposed image deconvolution method. This method uses a series of convolution operations to deconvolute image. We demonstrated that the method has the similar deconvolution results to the state-of-art method. The core calculation of the proposed method is image convolution, and thus our method can easily be integrated into GPU mode for large-scale image deconvolution.

Properties on n-dimensional convolution for image deconvolution

http://arxiv.org/pdf/1711.11224v1

Title Properties ndimensional convolution image deconvolution Summary Convolution system linear time invariant describe optical imaging process Based convolution system many deconvolution technique developed optical image analysis boosting space resolution optical image image denoising image enhancement gave property Ndimensional convolution using property proposed image deconvolution method method us series convolution operation deconvolute image demonstrated method similar deconvolution result stateofart method core calculation proposed method image convolution thus method easily integrated GPU mode largescale image deconvolution Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Akisato Kimura', 'Ichiro Takahashi', 'Masaomi Tanaka', 'Naoki Yasuda', 'Naonori Ueda', 'Naoki Yoshida']

1711.11526v1

Supernovae Type-Ia (SNeIa) play a significant role in exploring the history of the expansion of the Universe, since they are the best-known standard candles with which we can accurately measure the distance to the objects. Finding large samples of SNeIa and investigating their detailed characteristics have become an important issue in cosmology and astronomy. Existing methods relied on a photometric approach that first measures the luminance of supernova candidates precisely and then fits the results to a parametric function of temporal changes in luminance. However, it inevitably requires multi-epoch observations and complex luminance measurements. In this work, we present a novel method for classifying SNeIa simply from single-epoch observation images without any complex measurements, by effectively integrating the state-of-the-art computer vision methodology into the standard photometric approach. Our method first builds a convolutional neural network for estimating the luminance of supernovae from telescope images, and then constructs another neural network for the classification, where the estimated luminance and observation dates are used as features for classification. Both of the neural networks are integrated into a single deep neural network to classify SNeIa directly from observation images. Experimental results show the effectiveness of the proposed method and reveal classification performance comparable to existing photometric methods with multi-epoch observations.

Single-epoch supernova classification with deep convolutional neural networks

http://arxiv.org/pdf/1711.11526v1

Title Singleepoch supernova classification deep convolutional neural network Summary Supernovae TypeIa SNeIa play significant role exploring history expansion Universe since bestknown standard candle accurately measure distance object Finding large sample SNeIa investigating detailed characteristic become important issue cosmology astronomy Existing method relied photometric approach first measure luminance supernova candidate precisely fit result parametric function temporal change luminance However inevitably requires multiepoch observation complex luminance measurement work present novel method classifying SNeIa simply singleepoch observation image without complex measurement effectively integrating stateoftheart computer vision methodology standard photometric approach method first build convolutional neural network estimating luminance supernova telescope image construct another neural network classification estimated luminance observation date used feature classification neural network integrated single deep neural network classify SNeIa directly observation image Experimental result show effectiveness proposed method reveal classification performance comparable existing photometric method multiepoch observation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Stanislav Fort']

1712.00523v1

Supermassive black holes at centers of clusters of galaxies strongly interact with their host environment via AGN feedback. Key tracers of such activity are X-ray cavities -- regions of lower X-ray brightness within the cluster. We present an automatic method for detecting, and characterizing X-ray cavities in noisy, low-resolution X-ray images. We simulate clusters of galaxies, insert cavities into them, and produce realistic low-quality images comparable to observations at high redshifts. We then train a custom-built convolutional neural network to generate pixel-wise analysis of presence of cavities in a cluster. A ResNet architecture is then used to decode radii of cavities from the pixel-wise predictions. We surpass the accuracy, stability, and speed of current visual inspection based methods on simulated data.

Towards understanding feedback from supermassive black holes using convolutional neural networks

http://arxiv.org/pdf/1712.00523v1

Title Towards understanding feedback supermassive black hole using convolutional neural network Summary Supermassive black hole center cluster galaxy strongly interact host environment via AGN feedback Key tracer activity Xray cavity region lower Xray brightness within cluster present automatic method detecting characterizing Xray cavity noisy lowresolution Xray image simulate cluster galaxy insert cavity produce realistic lowquality image comparable observation high redshift train custombuilt convolutional neural network generate pixelwise analysis presence cavity cluster ResNet architecture used decode radius cavity pixelwise prediction surpass accuracy stability speed current visual inspection based method simulated data Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Xingyu Chen', 'Junzhi Yu', 'Shihan Kong', 'Zhengxing Wu', 'Xi Fang', 'Li Wen']

1712.00736v3

Underwater machine vision has attracted significant attention, but its low quality has prevented it from a wide range of applications. Although many different algorithms have been developed to solve this problem, real-time adaptive methods are frequently deficient. In this paper, based on filtering and the use of generative adversarial networks (GANs), two approaches are proposed for the aforementioned issue, i.e., a filtering-based restoration scheme (FRS) and a GAN-based restoration scheme (GAN-RS). Distinct from previous methods, FRS restores underwater images in the Fourier domain, which is composed of a parameter search, filtering, and enhancement. Aiming to further improve the image quality, GAN-RS can adaptively restore underwater machine vision in real time without the need for pretreatment. In particular, information in the Lab color space and the dark channel is developed as loss functions, namely, underwater index loss and dark channel prior loss, respectively. More specifically, learning from the underwater index, the discriminator is equipped with a carefully crafted underwater branch to predict the underwater probability of an image. A multi-stage loss strategy is then developed to guarantee the effective training of GANs. Through extensive comparisons on the image quality and applications, the superiority of the proposed approaches is confirmed. Consequently, the GAN-RS is considerably faster and achieves a state-of-the-art performance in terms of the color correction, contrast stretch, dehazing, and feature restoration of various underwater scenes. The source code will be made available.

Towards Quality Advancement of Underwater Machine Vision with Generative Adversarial Networks

http://arxiv.org/pdf/1712.00736v3

Title Towards Quality Advancement Underwater Machine Vision Generative Adversarial Networks Summary Underwater machine vision attracted significant attention low quality prevented wide range application Although many different algorithm developed solve problem realtime adaptive method frequently deficient paper based filtering use generative adversarial network GANs two approach proposed aforementioned issue ie filteringbased restoration scheme FRS GANbased restoration scheme GANRS Distinct previous method FRS restores underwater image Fourier domain composed parameter search filtering enhancement Aiming improve image quality GANRS adaptively restore underwater machine vision real time without need pretreatment particular information Lab color space dark channel developed loss function namely underwater index loss dark channel prior loss respectively specifically learning underwater index discriminator equipped carefully crafted underwater branch predict underwater probability image multistage loss strategy developed guarantee effective training GANs extensive comparison image quality application superiority proposed approach confirmed Consequently GANRS considerably faster achieves stateoftheart performance term color correction contrast stretch dehazing feature restoration various underwater scene source code made 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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['Daniel Scharstein', 'Tatsunori Taniai', 'Sudipta N. Sinha']

1712.00818v1

Semi-Global Matching (SGM) is a widely-used efficient stereo matching technique. It works well for textured scenes, but fails on untextured slanted surfaces due to its fronto-parallel smoothness assumption. To remedy this problem, we propose a simple extension, termed SGM-P, to utilize precomputed surface orientation priors. Such priors favor different surface slants in different 2D image regions or 3D scene regions and can be derived in various ways. In this paper we evaluate plane orientation priors derived from stereo matching at a coarser resolution and show that such priors can yield significant performance gains for difficult weakly-textured scenes. We also explore surface normal priors derived from Manhattan-world assumptions, and we analyze the potential performance gains using oracle priors derived from ground-truth data. SGM-P only adds a minor computational overhead to SGM and is an attractive alternative to more complex methods employing higher-order smoothness terms.

Semi-Global Stereo Matching with Surface Orientation Priors

http://arxiv.org/pdf/1712.00818v1

Title SemiGlobal Stereo Matching Surface Orientation Priors Summary SemiGlobal Matching SGM widelyused efficient stereo matching technique work well textured scene fails untextured slanted surface due frontoparallel smoothness assumption remedy problem propose simple extension termed SGMP utilize precomputed surface orientation prior prior favor different surface slant different 2D image region 3D scene region derived various way paper evaluate plane orientation prior derived stereo matching coarser resolution show prior yield significant performance gain difficult weaklytextured scene also explore surface normal prior derived Manhattanworld assumption analyze potential performance gain using oracle prior derived groundtruth data SGMP add minor computational overhead SGM attractive alternative complex method employing higherorder smoothness term Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jamie R. Nuñez', 'Christopher R. Anderton', 'Ryan S. Renslow']

1712.01662v2

Color vision deficiency (CVD) affects 8.5% of the population and leads to a different visual perception of colors. Though this has been known for decades, colormaps with many colors across the visual spectra are often used to represent data, leading to the potential for misinterpretation or difficulty with interpretation by someone with this deficiency. Until the creation of the module presented here, there were no colormaps mathematically optimized for CVD using modern color appearance models. While there have been some attempts to make aesthetically pleasing or subjectively tolerable colormaps for those with CVD, our goal was to make optimized colormaps for the most accurate perception of scientific data by as many viewers as possible. We developed a Python module, cmaputil, to create CVD-optimized colormaps, which imports colormaps and modifies them to be perceptually uniform in CVD-safe colorspace while linearizing and maximizing the brightness range. The module is made available to the science community to enable others to easily create their own CVD-optimized colormaps. Here, we present an example CVD-optimized colormap created with this module that is optimized for viewing by those without a CVD as well as those with red-green colorblindness. This colormap, cividis, enables nearly-identical visual-data interpretation to both groups, is perceptually uniform in hue and brightness, and increases in brightness linearly.

Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data

http://arxiv.org/pdf/1712.01662v2

Title Optimizing colormaps consideration color vision deficiency enable accurate interpretation scientific data Summary Color vision deficiency CVD affect 85 population lead different visual perception color Though known decade colormaps many color across visual spectrum often used represent data leading potential misinterpretation difficulty interpretation someone deficiency creation module presented colormaps mathematically optimized CVD using modern color appearance model attempt make aesthetically pleasing subjectively tolerable colormaps CVD goal make optimized colormaps accurate perception scientific data many viewer possible developed Python module cmaputil create CVDoptimized colormaps import colormaps modifies perceptually uniform CVDsafe colorspace linearizing maximizing brightness range module made available science community enable others easily create CVDoptimized colormaps present example CVDoptimized colormap created module optimized viewing without CVD well redgreen colorblindness colormap cividis enables nearlyidentical visualdata interpretation group perceptually uniform hue brightness increase brightness linearly Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Wellington Pinheiro dos Santos', 'Francisco Marcos de Assis']

1712.01696v1

The unsupervised classification has a very important role in the analysis of multispectral images, given its ability to assist the extraction of a priori knowledge of images. Algorithms like k-means and fuzzy c-means has long been used in this task. Computational Intelligence has proven to be an important field to assist in building classifiers optimized according to the quality of the grouping of classes and the evaluation of the quality of vector quantization. Several studies have shown that Philosophy, especially the Dialectical Method, has served as an important inspiration for the construction of new computational methods. This paper presents an evaluation of four methods based on the Dialectics: the Objective Dialectical Classifier and the Dialectical Optimization Method adapted to build a version of k-means with optimal quality indices; each of them is presented in two versions: a canonical version and another version obtained by applying the Principle of Maximum Entropy. These methods were compared to k-means, fuzzy c-means and Kohonen's self-organizing maps. The results showed that the methods based on Dialectics are robust to noise, and quantization can achieve results as good as those obtained with the Kohonen map, considered an optimal quantizer.

Avaliação do método dialético na quantização de imagens multiespectrais

http://arxiv.org/pdf/1712.01696v1

Title Avaliação método dialético na quantização de imagens multiespectrais Summary unsupervised classification important role analysis multispectral image given ability assist extraction priori knowledge image Algorithms like kmeans fuzzy cmeans long used task Computational Intelligence proven important field assist building classifier optimized according quality grouping class evaluation quality vector quantization Several study shown Philosophy especially Dialectical Method served important inspiration construction new computational method paper present evaluation four method based Dialectics Objective Dialectical Classifier Dialectical Optimization Method adapted build version kmeans optimal quality index presented two version canonical version another version obtained applying Principle Maximum Entropy method compared kmeans fuzzy cmeans Kohonens selforganizing map result showed method based Dialectics robust noise quantization achieve result good obtained Kohonen map considered optimal quantizer Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Wellington Pinheiro dos Santos', 'Ricardo Emmanuel de Souza', 'Ascendino Flávio Dias e Silva', 'Plínio Batista dos Santos Filho']

1712.01700v1

Alzheimer's disease is the most common cause of dementia, yet difficult to accurately diagnose without the use of invasive techniques, particularly at the beginning of the disease. This work addresses the classification and analysis of multispectral synthetic images composed by diffusion-weighted magnetic resonance brain volumes for evaluation of the area of cerebrospinal fluid and its correlation with the progression of Alzheimer's disease. A 1.5 T MR imaging system was used to acquire all the images presented. The classification methods are based on multilayer perceptrons and classifiers of radial basis function networks. It is assumed that the classes of interest can be separated by hyperquadrics. A polynomial network of degree 2 is used to classify the original volumes, generating a ground-truth volume. The classification results are used to improve the usual analysis by the map of apparent diffusion coefficients.

Avaliação da doença de Alzheimer pela análise multiespectral de imagens DW-MR por redes RBF como alternativa aos mapas ADC

http://arxiv.org/pdf/1712.01700v1

Title Avaliação da doença de Alzheimer pela análise multiespectral de imagens DWMR por redes RBF como alternativa aos mapas ADC Summary Alzheimers disease common cause dementia yet difficult accurately diagnose without use invasive technique particularly beginning disease work address classification analysis multispectral synthetic image composed diffusionweighted magnetic resonance brain volume evaluation area cerebrospinal fluid correlation progression Alzheimers disease 15 MR imaging system used acquire image presented classification method based multilayer perceptrons classifier radial basis function network assumed class interest separated hyperquadrics polynomial network degree 2 used classify original volume generating groundtruth volume classification result used improve usual analysis map apparent diffusion coefficient Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Kexin Pei', 'Yinzhi Cao', 'Junfeng Yang', 'Suman Jana']

1712.01785v3

Due to the increasing usage of machine learning (ML) techniques in security- and safety-critical domains, such as autonomous systems and medical diagnosis, ensuring correct behavior of ML systems, especially for different corner cases, is of growing importance. In this paper, we propose a generic framework for evaluating security and robustness of ML systems using different real-world safety properties. We further design, implement and evaluate VeriVis, a scalable methodology that can verify a diverse set of safety properties for state-of-the-art computer vision systems with only blackbox access. VeriVis leverage different input space reduction techniques for efficient verification of different safety properties. VeriVis is able to find thousands of safety violations in fifteen state-of-the-art computer vision systems including ten Deep Neural Networks (DNNs) such as Inception-v3 and Nvidia's Dave self-driving system with thousands of neurons as well as five commercial third-party vision APIs including Google vision and Clarifai for twelve different safety properties. Furthermore, VeriVis can successfully verify local safety properties, on average, for around 31.7% of the test images. VeriVis finds up to 64.8x more violations than existing gradient-based methods that, unlike VeriVis, cannot ensure non-existence of any violations. Finally, we show that retraining using the safety violations detected by VeriVis can reduce the average number of violations up to 60.2%.

Towards Practical Verification of Machine Learning: The Case of Computer Vision Systems

http://arxiv.org/pdf/1712.01785v3

Title Towards Practical Verification Machine Learning Case Computer Vision Systems Summary Due increasing usage machine learning ML technique security safetycritical domain autonomous system medical diagnosis ensuring correct behavior ML system especially different corner case growing importance paper propose generic framework evaluating security robustness ML system using different realworld safety property design implement evaluate VeriVis scalable methodology verify diverse set safety property stateoftheart computer vision system blackbox access VeriVis leverage different input space reduction technique efficient verification different safety property VeriVis able find thousand safety violation fifteen stateoftheart computer vision system including ten Deep Neural Networks DNNs Inceptionv3 Nvidias Dave selfdriving system thousand neuron well five commercial thirdparty vision APIs including Google vision Clarifai twelve different safety property Furthermore VeriVis successfully verify local safety property average around 317 test image VeriVis find 648x violation existing gradientbased method unlike VeriVis cannot ensure nonexistence violation Finally show retraining using safety violation detected VeriVis reduce average number violation 602 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Aritra Dutta', 'Peter Richtarik']

1712.02249v1

We propose a surprisingly simple model for supervised video background estimation. Our model is based on $\ell_1$ regression. As existing methods for $\ell_1$ regression do not scale to high-resolution videos, we propose several simple and scalable methods for solving the problem, including iteratively reweighted least squares, a homotopy method, and stochastic gradient descent. We show through extensive experiments that our model and methods match or outperform the state-of-the-art online and batch methods in virtually all quantitative and qualitative measures.

Online and Batch Supervised Background Estimation via L1 Regression

http://arxiv.org/pdf/1712.02249v1

Title Online Batch Supervised Background Estimation via L1 Regression Summary propose surprisingly simple model supervised video background estimation model based ell1 regression existing method ell1 regression scale highresolution video propose several simple scalable method solving problem including iteratively reweighted least square homotopy method stochastic gradient descent show extensive experiment model method match outperform stateoftheart online batch method virtually quantitative qualitative 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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['Shabab Bazrafkan', 'Shejin Thavalengal', 'Peter Corcoran']

1712.02877v1

With the increasing imaging and processing capabilities of today's mobile devices, user authentication using iris biometrics has become feasible. However, as the acquisition conditions become more unconstrained and as image quality is typically lower than dedicated iris acquisition systems, the accurate segmentation of iris regions is crucial for these devices. In this work, an end to end Fully Convolutional Deep Neural Network (FCDNN) design is proposed to perform the iris segmentation task for lower-quality iris images. The network design process is explained in detail, and the resulting network is trained and tuned using several large public iris datasets. A set of methods to generate and augment suitable lower quality iris images from the high-quality public databases are provided. The network is trained on Near InfraRed (NIR) images initially and later tuned on additional datasets derived from visible images. Comprehensive inter-database comparisons are provided together with results from a selection of experiments detailing the effects of different tunings of the network. Finally, the proposed model is compared with SegNet-basic, and a near-optimal tuning of the network is compared to a selection of other state-of-art iris segmentation algorithms. The results show very promising performance from the optimized Deep Neural Networks design when compared with state-of-art techniques applied to the same lower quality datasets.

An End to End Deep Neural Network for Iris Segmentation in Unconstraint Scenarios

http://arxiv.org/pdf/1712.02877v1

Title End End Deep Neural Network Iris Segmentation Unconstraint Scenarios Summary increasing imaging processing capability today mobile device user authentication using iris biometrics become feasible However acquisition condition become unconstrained image quality typically lower dedicated iris acquisition system accurate segmentation iris region crucial device work end end Fully Convolutional Deep Neural Network FCDNN design proposed perform iris segmentation task lowerquality iris image network design process explained detail resulting network trained tuned using several large public iris datasets set method generate augment suitable lower quality iris image highquality public database provided network trained Near InfraRed NIR image initially later tuned additional datasets derived visible image Comprehensive interdatabase comparison provided together result selection experiment detailing effect different tuning network Finally proposed model compared SegNetbasic nearoptimal tuning network compared selection stateofart iris segmentation algorithm result show promising performance optimized Deep Neural Networks design compared stateofart technique applied lower quality 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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['Jameel Malik', 'Ahmed Elhayek', 'Didier Stricker']

1712.03121v1

Articulated hand pose estimation is a challenging task for human-computer interaction. The state-of-the-art hand pose estimation algorithms work only with one or a few subjects for which they have been calibrated or trained. Particularly, the hybrid methods based on learning followed by model fitting or model based deep learning do not explicitly consider varying hand shapes and sizes. In this work, we introduce a novel hybrid algorithm for estimating the 3D hand pose as well as bone-lengths of the hand skeleton at the same time, from a single depth image. The proposed CNN architecture learns hand pose parameters and scale parameters associated with the bone-lengths simultaneously. Subsequently, a new hybrid forward kinematics layer employs both parameters to estimate 3D joint positions of the hand. For end-to-end training, we combine three public datasets NYU, ICVL and MSRA-2015 in one unified format to achieve large variation in hand shapes and sizes. Among hybrid methods, our method shows improved accuracy over the state-of-the-art on the combined dataset and the ICVL dataset that contain multiple subjects. Also, our algorithm is demonstrated to work well with unseen images.

Simultaneous Hand Pose and Skeleton Bone-Lengths Estimation from a Single Depth Image

http://arxiv.org/pdf/1712.03121v1

Title Simultaneous Hand Pose Skeleton BoneLengths Estimation Single Depth Image Summary Articulated hand pose estimation challenging task humancomputer interaction stateoftheart hand pose estimation algorithm work one subject calibrated trained Particularly hybrid method based learning followed model fitting model based deep learning explicitly consider varying hand shape size work introduce novel hybrid algorithm estimating 3D hand pose well bonelengths hand skeleton time single depth image proposed CNN architecture learns hand pose parameter scale parameter associated bonelengths simultaneously Subsequently new hybrid forward kinematics layer employ parameter estimate 3D joint position hand endtoend training combine three public datasets NYU ICVL MSRA2015 one unified format achieve large variation hand shape size Among hybrid method method show improved accuracy stateoftheart combined dataset ICVL dataset contain multiple subject Also algorithm demonstrated work well unseen 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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['Utkarsh Mall', 'G. Roshan Lal', 'Siddhartha Chaudhuri', 'Parag Chaudhuri']

1712.03380v1

We present a deep, bidirectional, recurrent framework for cleaning noisy and incomplete motion capture data. It exploits temporal coherence and joint correlations to infer adaptive filters for each joint in each frame. A single model can be trained to denoise a heterogeneous mix of action types, under substantial amounts of noise. A signal that has both noise and gaps is preprocessed with a second bidirectional network that synthesizes missing frames from surrounding context. The approach handles a wide variety of noise types and long gaps, does not rely on knowledge of the noise distribution, and operates in a streaming setting. We validate our approach through extensive evaluations on noise both in joint angles and in joint positions, and show that it improves upon various alternatives.

A Deep Recurrent Framework for Cleaning Motion Capture Data

http://arxiv.org/pdf/1712.03380v1

Title Deep Recurrent Framework Cleaning Motion Capture Data Summary present deep bidirectional recurrent framework cleaning noisy incomplete motion capture data exploit temporal coherence joint correlation infer adaptive filter joint frame single model trained denoise heterogeneous mix action type substantial amount noise signal noise gap preprocessed second bidirectional network synthesizes missing frame surrounding context approach handle wide variety noise type long gap rely knowledge noise distribution operates streaming setting validate approach extensive evaluation noise joint angle joint position show improves upon various alternative Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Rui Chen', 'Changshui Yang', 'Huizhu Jia', 'Xiaodong Xie']

1712.03381v1

In this letter, we address the problem of estimating Gaussian noise level from the trained dictionaries in update stage. We first provide rigorous statistical analysis on the eigenvalue distributions of a sample covariance matrix. Then we propose an interval-bounded estimator for noise variance in high dimensional setting. To this end, an effective estimation method for noise level is devised based on the boundness and asymptotic behavior of noise eigenvalue spectrum. The estimation performance of our method has been guaranteed both theoretically and empirically. The analysis and experiment results have demonstrated that the proposed algorithm can reliably infer true noise levels, and outperforms the relevant existing methods.

Noise Level Estimation for Overcomplete Dictionary Learning Based on Tight Asymptotic Bounds

http://arxiv.org/pdf/1712.03381v1

Title Noise Level Estimation Overcomplete Dictionary Learning Based Tight Asymptotic Bounds Summary letter address problem estimating Gaussian noise level trained dictionary update stage first provide rigorous statistical analysis eigenvalue distribution sample covariance matrix propose intervalbounded estimator noise variance high dimensional setting end effective estimation method noise level devised based boundness asymptotic behavior noise eigenvalue spectrum estimation performance method guaranteed theoretically empirically analysis experiment result demonstrated proposed algorithm reliably infer true noise level outperforms relevant existing method Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['AmirAbbas Davari', 'Armin Häberle', 'Vincent Christlein', 'Andreas Maier', 'Christian Riess']

1712.03596v1

High-resolution imaging has delivered new prospects for detecting the material composition and structure of cultural treasures. Despite the various techniques for analysis, a significant diagnostic gap remained in the range of available research capabilities for works on paper. Old master drawings were mostly composed in a multi-step manner with various materials. This resulted in the overlapping of different layers which made the subjacent strata difficult to differentiate. The separation of stratified layers using imaging methods could provide insights into the artistic work processes and help answer questions about the object, its attribution, or in identifying forgeries. The pattern recognition procedure was tested with mock replicas to achieve the separation and the capability of displaying concealed red chalk under ink. In contrast to RGB-sensor based imaging, the multi- or hyperspectral technology allows accurate layer separation by recording the characteristic signatures of the material's reflectance. The risk of damage to the artworks as a result of the examination can be reduced by using combinations of defined spectra for lightning and image capturing. By guaranteeing the maximum level of readability, our results suggest that the technique can be applied to a broader range of objects and assist in diagnostic research into cultural treasures in the future.

Sketch Layer Separation in Multi-Spectral Historical Document Images

http://arxiv.org/pdf/1712.03596v1

Title Sketch Layer Separation MultiSpectral Historical Document Images Summary Highresolution imaging delivered new prospect detecting material composition structure cultural treasure Despite various technique analysis significant diagnostic gap remained range available research capability work paper Old master drawing mostly composed multistep manner various material resulted overlapping different layer made subjacent stratum difficult differentiate separation stratified layer using imaging method could provide insight artistic work process help answer question object attribution identifying forgery pattern recognition procedure tested mock replica achieve separation capability displaying concealed red chalk ink contrast RGBsensor based imaging multi hyperspectral technology allows accurate layer separation recording characteristic signature material reflectance risk damage artwork result examination reduced using combination defined spectrum lightning image capturing guaranteeing maximum level readability result suggest technique applied broader range object assist diagnostic research cultural treasure 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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['Jaeyoon Yoo', 'Yongjun Hong', 'YungKyun Noh', 'Sungroh Yoon']

1712.03742v5

Autonomous navigation has become an increasingly popular machine learning application. Recent advances in deep learning have also brought huge improvements to autonomous navigation. However, prior outdoor autonomous navigation methods depended on various expensive sensors or expensive and sometimes erroneously labeled real data. In this paper, we propose an autonomous navigation method that does not require expensive labeled real images and uses only a relatively inexpensive monocular camera. Our proposed method is based on (1) domain adaptation with an adversarial learning framework and (2) exploiting synthetic data from a simulator. To the best of the authors' knowledge, this is the first work to apply domain adaptation with adversarial networks to autonomous navigation. We present empirical results on navigation in outdoor courses using an unmanned aerial vehicle. The performance of our method is comparable to that of a supervised model with labeled real data, although our method does not require any label information for the real data. Our proposal includes a theoretical analysis that supports the applicability of our approach.

Domain Adaptation Using Adversarial Learning for Autonomous Navigation

http://arxiv.org/pdf/1712.03742v5

Title Domain Adaptation Using Adversarial Learning Autonomous Navigation Summary Autonomous navigation become increasingly popular machine learning application Recent advance deep learning also brought huge improvement autonomous navigation However prior outdoor autonomous navigation method depended various expensive sensor expensive sometimes erroneously labeled real data paper propose autonomous navigation method require expensive labeled real image us relatively inexpensive monocular camera proposed method based 1 domain adaptation adversarial learning framework 2 exploiting synthetic data simulator best author knowledge first work apply domain adaptation adversarial network autonomous navigation present empirical result navigation outdoor course using unmanned aerial vehicle performance method comparable supervised model labeled real data although method require label information real data proposal includes theoretical analysis support applicability 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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['Yaoguang Li', 'Wei Cui', 'Cong Wang']

1712.03792v1

The classification accuracy of electrocardiogram signal is often affected by diverse factors in which mislabeled training samples issue is one of the most influential problems. In order to mitigate this negative effect, the method of cross validation is introduced to identify the mislabeled samples. The method utilizes the cooperative advantages of different classifiers to act as a filter for the training samples. The filter removes the mislabeled training samples and retains the correctly labeled ones with the help of 10-fold cross validation. Consequently, a new training set is provided to the final classifiers to acquire higher classification accuracies. Finally, we numerically show the effectiveness of the proposed method with the MIT-BIH arrhythmia database.

Identifying the Mislabeled Training Samples of ECG Signals using Machine Learning

http://arxiv.org/pdf/1712.03792v1

Title Identifying Mislabeled Training Samples ECG Signals using Machine Learning Summary classification accuracy electrocardiogram signal often affected diverse factor mislabeled training sample issue one influential problem order mitigate negative effect method cross validation introduced identify mislabeled sample method utilizes cooperative advantage different classifier act filter training sample filter remove mislabeled training sample retains correctly labeled one help 10fold cross validation Consequently new training set provided final classifier acquire higher classification accuracy Finally numerically show effectiveness proposed method MITBIH arrhythmia database Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['AmirAbbas Davari', 'Tobias Lindenberger', 'Armin Häberle', 'Vincent Christlein', 'Andreas Maier', 'Christian Riess']

1712.04482v1

In this work, we conducted a survey on different registration algorithms and investigated their suitability for hyperspectral historical image registration applications. After the evaluation of different algorithms, we choose an intensity based registration algorithm with a curved transformation model. For the transformation model, we select cubic B-splines since they should be capable to cope with all non-rigid deformations in our hyperspectral images. From a number of similarity measures, we found that residual complexity and localized mutual information are well suited for the task at hand. In our evaluation, both measures show an acceptable performance in handling all difficulties, e.g., capture range, non-stationary and spatially varying intensity distortions or multi-modality that occur in our application.

Image Registration for the Alignment of Digitized Historical Documents

http://arxiv.org/pdf/1712.04482v1

Title Image Registration Alignment Digitized Historical Documents Summary work conducted survey different registration algorithm investigated suitability hyperspectral historical image registration application evaluation different algorithm choose intensity based registration algorithm curved transformation model transformation model select cubic Bsplines since capable cope nonrigid deformation hyperspectral image number similarity measure found residual complexity localized mutual information well suited task hand evaluation measure show acceptable performance handling difficulty eg capture range nonstationary spatially varying intensity distortion multimodality occur 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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['Thushan Ganegedara', 'Lionel Ott', 'Fabio Ramos']

1712.05084v1

Adaptability is central to autonomy. Intuitively, for high-dimensional learning problems such as navigating based on vision, internal models with higher complexity allow to accurately encode the information available. However, most learning methods rely on models with a fixed structure and complexity. In this paper, we present a self-supervised framework for robots to learn to navigate, without any prior knowledge of the environment, by incrementally building the structure of a deep network as new data becomes available. Our framework captures images from a monocular camera and self labels the images to continuously train and predict actions from a computationally efficient adaptive deep architecture based on Autoencoders (AE), in a self-supervised fashion. The deep architecture, named Reinforced Adaptive Denoising Autoencoders (RA-DAE), uses reinforcement learning to dynamically change the network structure by adding or removing neurons. Experiments were conducted in simulation and real-world indoor and outdoor environments to assess the potential of self-supervised navigation. RA-DAE demonstrates better performance than equivalent non-adaptive deep learning alternatives and can continue to expand its knowledge, trading-off past and present information.

Learning to Navigate by Growing Deep Networks

http://arxiv.org/pdf/1712.05084v1

Title Learning Navigate Growing Deep Networks Summary Adaptability central autonomy Intuitively highdimensional learning problem navigating based vision internal model higher complexity allow accurately encode information available However learning method rely model fixed structure complexity paper present selfsupervised framework robot learn navigate without prior knowledge environment incrementally building structure deep network new data becomes available framework capture image monocular camera self label image continuously train predict action computationally efficient adaptive deep architecture based Autoencoders AE selfsupervised fashion deep architecture named Reinforced Adaptive Denoising Autoencoders RADAE us reinforcement learning dynamically change network structure adding removing neuron Experiments conducted simulation realworld indoor outdoor environment ass potential selfsupervised navigation RADAE demonstrates better performance equivalent nonadaptive deep learning alternative continue expand knowledge tradingoff past present information Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Tai-Xiang Jiang', 'Ting-Zhu Huang', 'Xi-Le Zhao', 'Liang-Jian Deng']

1712.05870v2

A recently developed novel tensor decomposition scheme named tensor singular value decomposition (t-SVD) results in a notion of rank referred to as the tubal-rank. Many methods minimize its convex surrogate the tensor nuclear norm (TNN) to enhance the low tubal-rankness of the underlying data. Generally, minimizing the TNN may cause some biases. In this paper, to alleviate these bias phenomenons, we consider to minimize the proposed partial sum of the tensor nuclear norm (PSTNN) in place of the TNN. The novel PSTNN is applied to the tasks of tensor completion and tensor principal component analysis. Numerical experiments are conducted on the synthetic data and real-world data, and experimental results reveal the effectiveness of the proposed methods.

A novel nonconvex approach to recover the low-tubal-rank tensor data: when t-SVD meets PSSV

http://arxiv.org/pdf/1712.05870v2

Title novel nonconvex approach recover lowtubalrank tensor data tSVD meet PSSV Summary recently developed novel tensor decomposition scheme named tensor singular value decomposition tSVD result notion rank referred tubalrank Many method minimize convex surrogate tensor nuclear norm TNN enhance low tubalrankness underlying data Generally minimizing TNN may cause bias paper alleviate bias phenomenon consider minimize proposed partial sum tensor nuclear norm PSTNN place TNN novel PSTNN applied task tensor completion tensor principal component analysis Numerical experiment conducted synthetic data realworld data experimental result reveal effectiveness 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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['Timo Hinzmann', 'Tim Taubner', 'Roland Siegwart']

1712.06837v2

This paper proposes a computationally efficient method to estimate the time-varying relative pose between two visual-inertial sensor rigs mounted on the flexible wings of a fixed-wing unmanned aerial vehicle (UAV). The estimated relative poses are used to generate highly accurate depth maps in real-time and can be employed for obstacle avoidance in low-altitude flights or landing maneuvers. The approach is structured as follows: Initially, a wing model is identified by fitting a probability density function to measured deviations from the nominal relative baseline transformation. At run-time, the prior knowledge about the wing model is fused in an Extended Kalman filter~(EKF) together with relative pose measurements obtained from solving a relative perspective N-point problem (PNP), and the linear accelerations and angular velocities measured by the two inertial measurement units (IMU) which are rigidly attached to the cameras. Results obtained from extensive synthetic experiments demonstrate that our proposed framework is able to estimate highly accurate baseline transformations and depth maps.

Flexible Stereo: Constrained, Non-rigid, Wide-baseline Stereo Vision for Fixed-wing Aerial Platforms

http://arxiv.org/pdf/1712.06837v2

Title Flexible Stereo Constrained Nonrigid Widebaseline Stereo Vision Fixedwing Aerial Platforms Summary paper proposes computationally efficient method estimate timevarying relative pose two visualinertial sensor rig mounted flexible wing fixedwing unmanned aerial vehicle UAV estimated relative pose used generate highly accurate depth map realtime employed obstacle avoidance lowaltitude flight landing maneuver approach structured follows Initially wing model identified fitting probability density function measured deviation nominal relative baseline transformation runtime prior knowledge wing model fused Extended Kalman filterEKF together relative pose measurement obtained solving relative perspective Npoint problem PNP linear acceleration angular velocity measured two inertial measurement unit IMU rigidly attached camera Results obtained extensive synthetic experiment demonstrate proposed framework able estimate highly accurate baseline transformation depth map Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Zhexiong Shang', 'Zhigang Shen']

1712.07122v1

3D reconstruction can be used as a platform to monitor the performance of activities on construction site, such as construction progress monitoring, structure inspection and post-disaster rescue. Comparing to other sensors, RGB image has the advantages of low-cost, texture rich and easy to implement that has been used as the primary method for 3D reconstruction in construction industry. However, the image-based 3D reconstruction always requires extended time to acquire and/or to process the image data, which limits its application on time critical projects. Recent progress in Visual Simultaneous Localization and Mapping (SLAM) make it possible to reconstruct a 3D map of construction site in real-time. Integrated with Unmanned Aerial Vehicle (UAV), the obstacles areas that are inaccessible for the ground equipment can also be sensed. Despite these advantages of visual SLAM and UAV, until now, such technique has not been fully investigated on construction site. Therefore, the objective of this research is to present a pilot study of using visual SLAM and UAV for real-time construction site reconstruction. The system architecture and the experimental setup are introduced, and the preliminary results and the potential applications using Visual SLAM and UAV on construction site are discussed.

Real-time 3D Reconstruction on Construction Site using Visual SLAM and UAV

http://arxiv.org/pdf/1712.07122v1

Title Realtime 3D Reconstruction Construction Site using Visual SLAM UAV Summary 3D reconstruction used platform monitor performance activity construction site construction progress monitoring structure inspection postdisaster rescue Comparing sensor RGB image advantage lowcost texture rich easy implement used primary method 3D reconstruction construction industry However imagebased 3D reconstruction always requires extended time acquire andor process image data limit application time critical project Recent progress Visual Simultaneous Localization Mapping SLAM make possible reconstruct 3D map construction site realtime Integrated Unmanned Aerial Vehicle UAV obstacle area inaccessible ground equipment also sensed Despite advantage visual SLAM UAV technique fully investigated construction site Therefore objective research present pilot study using visual SLAM UAV realtime construction site reconstruction system architecture experimental setup introduced preliminary result potential application using Visual SLAM UAV construction site discussed Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher 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 Chen', 'Yanjun Xie', 'Jie Sun', 'Niranjan Balu', 'Mahmud Mossa-Basha', 'Kristi Pimentel', 'Thomas S. Hatsukami', 'Jenq-Neng Hwang', 'Chun Yuan']

1712.07194v1

Automated segmentation of intracranial arteries on magnetic resonance angiography (MRA) allows for quantification of cerebrovascular features, which provides tools for understanding aging and pathophysiological adaptations of the cerebrovascular system. Using a convolutional autoencoder (CAE) for segmentation is promising as it takes advantage of the autoencoder structure in effective noise reduction and feature extraction by representing high dimensional information with low dimensional latent variables. In this report, an optimized CAE model (Y-net) was trained to learn a 3D segmentation model of intracranial arteries from 49 cases of MRA data. The trained model was shown to perform better than the three traditional segmentation methods in both binary classification and visual evaluation.

Y-net: 3D intracranial artery segmentation using a convolutional autoencoder

http://arxiv.org/pdf/1712.07194v1

Title Ynet 3D intracranial artery segmentation using convolutional autoencoder Summary Automated segmentation intracranial artery magnetic resonance angiography MRA allows quantification cerebrovascular feature provides tool understanding aging pathophysiological adaptation cerebrovascular system Using convolutional autoencoder CAE segmentation promising take advantage autoencoder structure effective noise reduction feature extraction representing high dimensional information low dimensional latent variable report optimized CAE model Ynet trained learn 3D segmentation model intracranial artery 49 case MRA data trained model shown perform better three traditional segmentation method binary classification visual 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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['Viral Parekh', 'Pin Sym Foong', 'Shendong Zhao', 'Ramanathan Subramanian']

1712.08084v1

Engagement in dementia is typically measured using behavior observational scales (BOS) that are tedious and involve intensive manual labor to annotate, and are therefore not easily scalable. We propose AVEID, a low cost and easy-to-use video-based engagement measurement tool to determine the engagement level of a person with dementia (PwD) during digital interaction. We show that the objective behavioral measures computed via AVEID correlate well with subjective expert impressions for the popular MPES and OME BOS, confirming its viability and effectiveness. Moreover, AVEID measures can be obtained for a variety of engagement designs, thereby facilitating large-scale studies with PwD populations.

AVEID: Automatic Video System for Measuring Engagement In Dementia

http://arxiv.org/pdf/1712.08084v1

Title AVEID Automatic Video System Measuring Engagement Dementia Summary Engagement dementia typically measured using behavior observational scale BOS tedious involve intensive manual labor annotate therefore easily scalable propose AVEID low cost easytouse videobased engagement measurement tool determine engagement level person dementia PwD digital interaction show objective behavioral measure computed via AVEID correlate well subjective expert impression popular MPES OME BOS confirming viability effectiveness Moreover AVEID measure obtained variety engagement design thereby facilitating largescale study PwD population Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Nils Bore', 'Johan Ekekrantz', 'Patric Jensfelt', 'John Folkesson']

1712.08409v2

This paper studies the problem of detection and tracking of general objects with long-term dynamics, observed by a mobile robot moving in a large environment. A key problem is that due to the environment scale, it can only observe a subset of the objects at any given time. Since some time passes between observations of objects in different places, the objects might be moved when the robot is not there. We propose a model for this movement in which the objects typically only move locally, but with some small probability they jump longer distances, through what we call global motion. For filtering, we decompose the posterior over local and global movements into two linked processes. The posterior over the global movements and measurement associations is sampled, while we track the local movement analytically using Kalman filters. This novel filter is evaluated on point cloud data gathered autonomously by a mobile robot over an extended period of time. We show that tracking jumping objects is feasible, and that the proposed probabilistic treatment outperforms previous methods when applied to real world data. The key to efficient probabilistic tracking in this scenario is focused sampling of the object posteriors.

Detection and Tracking of General Movable Objects in Large 3D Maps

http://arxiv.org/pdf/1712.08409v2

Title Detection Tracking General Movable Objects Large 3D Maps Summary paper study problem detection tracking general object longterm dynamic observed mobile robot moving large environment key problem due environment scale observe subset object given time Since time pass observation object different place object might moved robot propose model movement object typically move locally small probability jump longer distance call global motion filtering decompose posterior local global movement two linked process posterior global movement measurement association sampled track local movement analytically using Kalman filter novel filter evaluated point cloud data gathered autonomously mobile robot extended period time show tracking jumping object feasible proposed probabilistic treatment outperforms previous method applied real world data key efficient probabilistic tracking scenario focused sampling object posterior Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Xianzhi Du', 'Larry Davis']

1712.08675v1

Compared to the general semantic segmentation problem, portrait segmentation has higher precision requirement on boundary area. However, this problem has not been well studied in previous works. In this paper, we propose a boundary-sensitive deep neural network (BSN) for portrait segmentation. BSN introduces three novel techniques. First, an individual boundary-sensitive kernel is proposed by dilating the contour line and assigning the boundary pixels with multi-class labels. Second, a global boundary-sensitive kernel is employed as a position sensitive prior to further constrain the overall shape of the segmentation map. Third, we train a boundary-sensitive attribute classifier jointly with the segmentation network to reinforce the network with semantic boundary shape information. We have evaluated BSN on the current largest public portrait segmentation dataset,~\ie, the PFCN dataset, as well as the portrait images collected from other three popular image segmentation datasets: COCO, COCO-Stuff, and PASCAL VOC. Our method achieves the superior quantitative and qualitative performance over state-of-the-arts on all the datasets, especially on the boundary area.

Boundary-sensitive Network for Portrait Segmentation

http://arxiv.org/pdf/1712.08675v1

Title Boundarysensitive Network Portrait Segmentation Summary Compared general semantic segmentation problem portrait segmentation higher precision requirement boundary area However problem well studied previous work paper propose boundarysensitive deep neural network BSN portrait segmentation BSN introduces three novel technique First individual boundarysensitive kernel proposed dilating contour line assigning boundary pixel multiclass label Second global boundarysensitive kernel employed position sensitive prior constrain overall shape segmentation map Third train boundarysensitive attribute classifier jointly segmentation network reinforce network semantic boundary shape information evaluated BSN current largest public portrait segmentation datasetie PFCN dataset well portrait image collected three popular image segmentation datasets COCO COCOStuff PASCAL VOC method achieves superior quantitative qualitative performance stateofthearts datasets especially boundary area Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Anurag Ghosh', 'Suriya Singh', 'C. V. Jawahar']

1712.08714v1

Sports video data is recorded for nearly every major tournament but remains archived and inaccessible to large scale data mining and analytics. It can only be viewed sequentially or manually tagged with higher-level labels which is time consuming and prone to errors. In this work, we propose an end-to-end framework for automatic attributes tagging and analysis of sport videos. We use commonly available broadcast videos of matches and, unlike previous approaches, does not rely on special camera setups or additional sensors. Our focus is on Badminton as the sport of interest. We propose a method to analyze a large corpus of badminton broadcast videos by segmenting the points played, tracking and recognizing the players in each point and annotating their respective badminton strokes. We evaluate the performance on 10 Olympic matches with 20 players and achieved 95.44% point segmentation accuracy, 97.38% player detection score (mAP@0.5), 97.98% player identification accuracy, and stroke segmentation edit scores of 80.48%. We further show that the automatically annotated videos alone could enable the gameplay analysis and inference by computing understandable metrics such as player's reaction time, speed, and footwork around the court, etc.

Towards Structured Analysis of Broadcast Badminton Videos

http://arxiv.org/pdf/1712.08714v1

Title Towards Structured Analysis Broadcast Badminton Videos Summary Sports video data recorded nearly every major tournament remains archived inaccessible large scale data mining analytics viewed sequentially manually tagged higherlevel label time consuming prone error work propose endtoend framework automatic attribute tagging analysis sport video use commonly available broadcast video match unlike previous approach rely special camera setup additional sensor focus Badminton sport interest propose method analyze large corpus badminton broadcast video segmenting point played tracking recognizing player point annotating respective badminton stroke evaluate performance 10 Olympic match 20 player achieved 9544 point segmentation accuracy 9738 player detection score mAP05 9798 player identification accuracy stroke segmentation edit score 8048 show automatically annotated video alone could enable gameplay analysis inference computing understandable metric player reaction time speed footwork around court etc Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jianwei Zhang', 'Xu Chen', 'Xuezhong Xiao']

1712.08776v1

To solve the issue of segmenting rich texture images, a novel detection methods based on the affine invariable principle is proposed. Considering the similarity between the texture areas, we first take the affine transform to get numerous shapes, and utilize the KLT algorithm to verify the similarity. The transforms include rotation, proportional transformation and perspective deformation to cope with a variety of situations. Then we propose an improved LBP method combining canny edge detection to handle the boundary in the segmentation process. Moreover, human-computer interaction of this method which helps splitting the matched texture area from the original images is user-friendly.

Texture Object Segmentation Based on Affine Invariant Texture Detection

http://arxiv.org/pdf/1712.08776v1

Title Texture Object Segmentation Based Affine Invariant Texture Detection Summary solve issue segmenting rich texture image novel detection method based affine invariable principle proposed Considering similarity texture area first take affine transform get numerous shape utilize KLT algorithm verify similarity transforms include rotation proportional transformation perspective deformation cope variety situation propose improved LBP method combining canny edge detection handle boundary segmentation process Moreover humancomputer interaction method help splitting matched texture area original image userfriendly Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Thiago Santini', 'Wolfgang Fuhl', 'Enkelejda Kasneci']

1712.08900v1

Real-time, accurate, and robust pupil detection is an essential prerequisite to enable pervasive eye-tracking and its applications -- e.g., gaze-based human computer interaction, health monitoring, foveated rendering, and advanced driver assistance. However, automated pupil detection has proved to be an intricate task in real-world scenarios due to a large mixture of challenges such as quickly changing illumination and occlusions. In this paper, we introduce the Pupil Reconstructor PuRe, a method for pupil detection in pervasive scenarios based on a novel edge segment selection and conditional segment combination schemes; the method also includes a confidence measure for the detected pupil. The proposed method was evaluated on over 316,000 images acquired with four distinct head-mounted eye tracking devices. Results show a pupil detection rate improvement of over 10 percentage points w.r.t. state-of-the-art algorithms in the two most challenging data sets (6.46 for all data sets), further pushing the envelope for pupil detection. Moreover, we advance the evaluation protocol of pupil detection algorithms by also considering eye images in which pupils are not present. In this aspect, PuRe improved precision and specificity w.r.t. state-of-the-art algorithms by 25.05 and 10.94 percentage points, respectively, demonstrating the meaningfulness of PuRe's confidence measure. PuRe operates in real-time for modern eye trackers (at 120 fps).

PuRe: Robust pupil detection for real-time pervasive eye tracking

http://arxiv.org/pdf/1712.08900v1

Title PuRe Robust pupil detection realtime pervasive eye tracking Summary Realtime accurate robust pupil detection essential prerequisite enable pervasive eyetracking application eg gazebased human computer interaction health monitoring foveated rendering advanced driver assistance However automated pupil detection proved intricate task realworld scenario due large mixture challenge quickly changing illumination occlusion paper introduce Pupil Reconstructor PuRe method pupil detection pervasive scenario based novel edge segment selection conditional segment combination scheme method also includes confidence measure detected pupil proposed method evaluated 316000 image acquired four distinct headmounted eye tracking device Results show pupil detection rate improvement 10 percentage point wrt stateoftheart algorithm two challenging data set 646 data set pushing envelope pupil detection Moreover advance evaluation protocol pupil detection algorithm also considering eye image pupil present aspect PuRe improved precision specificity wrt stateoftheart algorithm 2505 1094 percentage point respectively demonstrating meaningfulness PuRes confidence measure PuRe operates realtime modern eye tracker 120 fps Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mohammad Amin Khorsandi', 'Nader Karimi', 'Shadrokh Samavi']

1712.10164v1

Capsule endoscopy is a novel and non-invasive method for diagnosis, which assists gastroenterologists to monitor the digestive track. Although this new technology has many advantages over the conventional endoscopy, there are weaknesses that limits the usage of this technology. Some weaknesses are due to using small-size batteries. Radio transmitter consumes the largest portion of energy; consequently, a simple way to reduce the power consumption is to reduce the data to be transmitted. Many works are proposed to reduce the amount of data to be transmitted consist of specific compression methods and reduction in video resolution and frame rate. We proposed a system inside the capsule for detecting informative frames and sending these frames instead of several non-informative frames. In this work, we specifically focused on hardware friendly algorithm (with capability of parallelism and pipeline) for implementation of polyp detection. Two features of positive contrast and customized edges of polyps are exploited to define whether the frame consists of polyp or not. The proposed method is devoid of complex and iterative structure to save power and reduce the response time. Experimental results indicate acceptable rate of detection of our work.

Polyp detection inside the capsule endoscopy: an approach for power consumption reduction

http://arxiv.org/pdf/1712.10164v1

Title Polyp detection inside capsule endoscopy approach power consumption reduction Summary Capsule endoscopy novel noninvasive method diagnosis assist gastroenterologist monitor digestive track Although new technology many advantage conventional endoscopy weakness limit usage technology weakness due using smallsize battery Radio transmitter consumes largest portion energy consequently simple way reduce power consumption reduce data transmitted Many work proposed reduce amount data transmitted consist specific compression method reduction video resolution frame rate proposed system inside capsule detecting informative frame sending frame instead several noninformative frame work specifically focused hardware friendly algorithm capability parallelism pipeline implementation polyp detection Two feature positive contrast customized edge polyp exploited define whether frame consists polyp proposed method devoid complex iterative structure save power reduce response time Experimental result indicate acceptable rate detection work Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['U. A. Nnolim']

1801.00098v2

This report presents the results of a partial differential equation (PDE)-based image enhancement algorithm, for dynamic range compression and illumination correction in the absence of the logarithmic function. The proposed algorithm combines forward and reverse flows in a PDE-based formulation. The experimental results are compared with algorithms from the literature and indicate comparable performance in most cases.

A PDE-based log-agnostic illumination correction algorithm

http://arxiv.org/pdf/1801.00098v2

Title PDEbased logagnostic illumination correction algorithm Summary report present result partial differential equation PDEbased image enhancement algorithm dynamic range compression illumination correction absence logarithmic function proposed algorithm combine forward reverse flow PDEbased formulation experimental result compared algorithm literature indicate comparable performance case Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mahmood Sharif', 'Sruti Bhagavatula', 'Lujo Bauer', 'Michael K. Reiter']

1801.00349v1

In this paper we show that misclassification attacks against face-recognition systems based on deep neural networks (DNNs) are more dangerous than previously demonstrated, even in contexts where the adversary can manipulate only her physical appearance (versus directly manipulating the image input to the DNN). Specifically, we show how to create eyeglasses that, when worn, can succeed in targeted (impersonation) or untargeted (dodging) attacks while improving on previous work in one or more of three facets: (i) inconspicuousness to onlooking observers, which we test through a user study; (ii) robustness of the attack against proposed defenses; and (iii) scalability in the sense of decoupling eyeglass creation from the subject who will wear them, i.e., by creating "universal" sets of eyeglasses that facilitate misclassification. Central to these improvements are adversarial generative nets, a method we propose to generate physically realizable attack artifacts (here, eyeglasses) automatically.

Adversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition

http://arxiv.org/pdf/1801.00349v1

Title Adversarial Generative Nets Neural Network Attacks StateoftheArt Face Recognition Summary paper show misclassification attack facerecognition system based deep neural network DNNs dangerous previously demonstrated even context adversary manipulate physical appearance versus directly manipulating image input DNN Specifically show create eyeglass worn succeed targeted impersonation untargeted dodging attack improving previous work one three facet inconspicuousness onlooking observer test user study ii robustness attack proposed defense iii scalability sense decoupling eyeglass creation subject wear ie creating universal set eyeglass facilitate misclassification Central improvement adversarial generative net method propose generate physically realizable attack artifact eyeglass automatically Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hamid Reza Shahdoosti']

1801.00635v1

Denoising of coefficients in a sparse domain (e.g. wavelet) has been researched extensively because of its simplicity and effectiveness. Literature mainly has focused on designing the best global threshold. However, this paper proposes a new denoising method using bivariate shrinkage function in framelet domain. In the proposed method, maximum aposteriori probability is used for estimate of the denoised coefficient and non-Gaussian bivariate function is applied to model the statistics of framelet coefficients. For every framelet coefficient, there is a corresponding threshold depending on the local statistics of framelet coefficients. Experimental results show that using bivariate shrinkage function in framelet domain yields significantly superior image quality and higher PSNR than some well-known denoising methods.

Image denoising through bivariate shrinkage function in framelet domain

http://arxiv.org/pdf/1801.00635v1

Title Image denoising bivariate shrinkage function framelet domain Summary Denoising coefficient sparse domain eg wavelet researched extensively simplicity effectiveness Literature mainly focused designing best global threshold However paper proposes new denoising method using bivariate shrinkage function framelet domain proposed method maximum aposteriori probability used estimate denoised coefficient nonGaussian bivariate function applied model statistic framelet coefficient every framelet coefficient corresponding threshold depending local statistic framelet coefficient Experimental result show using bivariate shrinkage function framelet domain yield significantly superior image quality higher PSNR wellknown denoising 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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['Yi Xiao', 'Xiang Cao', 'Xianyi Zhu', 'Renzhi Yang', 'Yan Zheng']

1801.00968v1

High-resolution depth map can be inferred from a low-resolution one with the guidance of an additional high-resolution texture map of the same scene. Recently, deep neural networks with large receptive fields are shown to benefit applications such as image completion. Our insight is that super resolution is similar to image completion, where only parts of the depth values are precisely known. In this paper, we present a joint convolutional neural pyramid model with large receptive fields for joint depth map super-resolution. Our model consists of three sub-networks, two convolutional neural pyramids concatenated by a normal convolutional neural network. The convolutional neural pyramids extract information from large receptive fields of the depth map and guidance map, while the convolutional neural network effectively transfers useful structures of the guidance image to the depth image. Experimental results show that our model outperforms existing state-of-the-art algorithms not only on data pairs of RGB/depth images, but also on other data pairs like color/saliency and color-scribbles/colorized images.

Joint convolutional neural pyramid for depth map super-resolution

http://arxiv.org/pdf/1801.00968v1

Title Joint convolutional neural pyramid depth map superresolution Summary Highresolution depth map inferred lowresolution one guidance additional highresolution texture map scene Recently deep neural network large receptive field shown benefit application image completion insight super resolution similar image completion part depth value precisely known paper present joint convolutional neural pyramid model large receptive field joint depth map superresolution model consists three subnetworks two convolutional neural pyramid concatenated normal convolutional neural network convolutional neural pyramid extract information large receptive field depth map guidance map convolutional neural network effectively transfer useful structure guidance image depth image Experimental result show model outperforms existing stateoftheart algorithm data pair RGBdepth image also data pair like colorsaliency colorscribblescolorized 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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['Timur M. Khanipov']

1801.01054v1

For the computational model where only additions are allowed, the $\Omega(n^2\log n)$ lower bound on operations count with respect to image size $n\times n$ is obtained for two types of the discrete Radon transform implementations: the fast Hough transform and a generic strip pattern class which includes the classical Hough transform, implying the fast Hough transform algorithm asymptotic optimality. The proofs are based on a specific result from the boolean circuits complexity theory and are generalized for the case of boolean $\vee$ binary operation.

Computational complexity lower bounds of certain discrete Radon transform approximations

http://arxiv.org/pdf/1801.01054v1

Title Computational complexity lower bound certain discrete Radon transform approximation Summary computational model addition allowed Omegan2log n lower bound operation count respect image size ntimes n obtained two type discrete Radon transform implementation fast Hough transform generic strip pattern class includes classical Hough transform implying fast Hough transform algorithm asymptotic optimality proof based specific result boolean circuit complexity theory generalized case boolean vee binary operation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Si Liu', 'Yao Sun', 'Defa Zhu', 'Guanghui Ren', 'Yu Chen', 'Jiashi Feng', 'Jizhong Han']

1801.01260v2

Human parsing has been extensively studied recently due to its wide applications in many important scenarios. Mainstream fashion parsing models focus on parsing the high-resolution and clean images. However, directly applying the parsers trained on benchmarks to a particular application scenario in the wild, e.g., a canteen, airport or workplace, often gives non-satisfactory performance due to domain shift. In this paper, we explore a new and challenging cross-domain human parsing problem: taking the benchmark dataset with extensive pixel-wise labeling as the source domain, how to obtain a satisfactory parser on a new target domain without requiring any additional manual labeling? To this end, we propose a novel and efficient cross-domain human parsing model to bridge the cross-domain differences in terms of visual appearance and environment conditions and fully exploit commonalities across domains. Our proposed model explicitly learns a feature compensation network, which is specialized for mitigating the cross-domain differences. A discriminative feature adversarial network is introduced to supervise the feature compensation to effectively reduce the discrepancy between feature distributions of two domains. Besides, our model also introduces a structured label adversarial network to guide the parsing results of the target domain to follow the high-order relationships of the structured labels shared across domains. The proposed framework is end-to-end trainable, practical and scalable in real applications. Extensive experiments are conducted where LIP dataset is the source domain and 4 different datasets including surveillance videos, movies and runway shows are evaluated as target domains. The results consistently confirm data efficiency and performance advantages of the proposed method for the cross-domain human parsing problem.

Cross-domain Human Parsing via Adversarial Feature and Label Adaptation

http://arxiv.org/pdf/1801.01260v2

Title Crossdomain Human Parsing via Adversarial Feature Label Adaptation Summary Human parsing extensively studied recently due wide application many important scenario Mainstream fashion parsing model focus parsing highresolution clean image However directly applying parser trained benchmark particular application scenario wild eg canteen airport workplace often give nonsatisfactory performance due domain shift paper explore new challenging crossdomain human parsing problem taking benchmark dataset extensive pixelwise labeling source domain obtain satisfactory parser new target domain without requiring additional manual labeling end propose novel efficient crossdomain human parsing model bridge crossdomain difference term visual appearance environment condition fully exploit commonality across domain proposed model explicitly learns feature compensation network specialized mitigating crossdomain difference discriminative feature adversarial network introduced supervise feature compensation effectively reduce discrepancy feature distribution two domain Besides model also introduces structured label adversarial network guide parsing result target domain follow highorder relationship structured label shared across domain proposed framework endtoend trainable practical scalable real application Extensive experiment conducted LIP dataset source domain 4 different datasets including surveillance video movie runway show evaluated target domain result consistently confirm data efficiency performance advantage proposed method crossdomain human parsing 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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['Anurag Ghosh', 'C. V. Jawahar']

1801.01430v1

In this paper, we demonstrate a score based indexing approach for tennis videos. Given a broadcast tennis video (BTV), we index all the video segments with their scores to create a navigable and searchable match. Our approach temporally segments the rallies in the video and then recognizes the scores from each of the segments, before refining the scores using the knowledge of the tennis scoring system. We finally build an interface to effortlessly retrieve and view the relevant video segments by also automatically tagging the segmented rallies with human accessible tags such as 'fault' and 'deuce'. The efficiency of our approach is demonstrated on BTV's from two major tennis tournaments.

SmartTennisTV: Automatic indexing of tennis videos

http://arxiv.org/pdf/1801.01430v1

Title SmartTennisTV Automatic indexing tennis video Summary paper demonstrate score based indexing approach tennis video Given broadcast tennis video BTV index video segment score create navigable searchable match approach temporally segment rally video recognizes score segment refining score using knowledge tennis scoring system finally build interface effortlessly retrieve view relevant video segment also automatically tagging segmented rally human accessible tag fault deuce efficiency approach demonstrated BTVs two major tennis tournament Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Nicolaie Popescu-Bodorin', 'Lucian Stefanita Grigore', 'Valentina Emilia Balas', 'Cristina Madalina Noaica', 'Ionut Axenie', 'Justinian Popa', 'Cristian Munteanu', 'Victor Stroescu', 'Ionut Manu', 'Alexandru Herea', 'Kartal Horasanli', 'Iulia Maria Motoc']

1801.01695v1

Cross-sensor comparison experimental results reported here show that the procedure defined and simulated during the Cross-Sensor Comparison Competition 2013 by our team for migrating / upgrading LG2200 based to LG4000 based biometric systems leads to better LG4000-to-LG2200 cross-sensor iris recognition results than previously reported, both in terms of user comfort and in terms of system safety. On the other hand, LG2200-to-LG400 migration/upgrade procedure defined and implemented by us is applicable to solve interoperability issues between LG2200 based and LG4000 based systems, but also to other pairs of systems having the same shift in the quality of acquired images.

Cross-Sensor Iris Recognition: LG4000-to-LG2200 Comparison

http://arxiv.org/pdf/1801.01695v1

Title CrossSensor Iris Recognition LG4000toLG2200 Comparison Summary Crosssensor comparison experimental result reported show procedure defined simulated CrossSensor Comparison Competition 2013 team migrating upgrading LG2200 based LG4000 based biometric system lead better LG4000toLG2200 crosssensor iris recognition result previously reported term user comfort term system safety hand LG2200toLG400 migrationupgrade procedure defined implemented u applicable solve interoperability issue LG2200 based LG4000 based system also pair system shift quality acquired 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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['Di Tang', 'Zhe Zhou', 'Yinqian Zhang', 'Kehuan Zhang']

1801.01949v1

Face authentication systems are becoming increasingly prevalent, especially with the rapid development of Deep Learning technologies. However, human facial information is easy to be captured and reproduced, which makes face authentication systems vulnerable to various attacks. Liveness detection is an important defense technique to prevent such attacks, but existing solutions did not provide clear and strong security guarantees, especially in terms of time. To overcome these limitations, we propose a new liveness detection protocol called Face Flashing that significantly increases the bar for launching successful attacks on face authentication systems. By randomly flashing well-designed pictures on a screen and analyzing the reflected light, our protocol has leveraged physical characteristics of human faces: reflection processing at the speed of light, unique textual features, and uneven 3D shapes. Cooperating with working mechanism of the screen and digital cameras, our protocol is able to detect subtle traces left by an attacking process. To demonstrate the effectiveness of Face Flashing, we implemented a prototype and performed thorough evaluations with large data set collected from real-world scenarios. The results show that our Timing Verification can effectively detect the time gap between legitimate authentications and malicious cases. Our Face Verification can also differentiate 2D plane from 3D objects accurately. The overall accuracy of our liveness detection system is 98.8\%, and its robustness was evaluated in different scenarios. In the worst case, our system's accuracy decreased to a still-high 97.3\%.

Face Flashing: a Secure Liveness Detection Protocol based on Light Reflections

http://arxiv.org/pdf/1801.01949v1

Title Face Flashing Secure Liveness Detection Protocol based Light Reflections Summary Face authentication system becoming increasingly prevalent especially rapid development Deep Learning technology However human facial information easy captured reproduced make face authentication system vulnerable various attack Liveness detection important defense technique prevent attack existing solution provide clear strong security guarantee especially term time overcome limitation propose new liveness detection protocol called Face Flashing significantly increase bar launching successful attack face authentication system randomly flashing welldesigned picture screen analyzing reflected light protocol leveraged physical characteristic human face reflection processing speed light unique textual feature uneven 3D shape Cooperating working mechanism screen digital camera protocol able detect subtle trace left attacking process demonstrate effectiveness Face Flashing implemented prototype performed thorough evaluation large data set collected realworld scenario result show Timing Verification effectively detect time gap legitimate authentication malicious case Face Verification also differentiate 2D plane 3D object accurately overall accuracy liveness detection system 988 robustness evaluated different scenario worst case system accuracy decreased stillhigh 973 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Luca Carlone', 'Giuseppe C. Calafiore']

1801.02112v1

Pose Graph Optimization involves the estimation of a set of poses from pairwise measurements and provides a formalization for many problems arising in mobile robotics and geometric computer vision. In this paper, we consider the case in which a subset of the measurements fed to pose graph optimization is spurious. Our first contribution is to develop robust estimators that can cope with heavy-tailed measurement noise, hence increasing robustness to the presence of outliers. Since the resulting estimators require solving nonconvex optimization problems, we further develop convex relaxations that approximately solve those problems via semidefinite programming. We then provide conditions under which the proposed relaxations are exact. Contrarily to existing approaches, our convex relaxations do not rely on the availability of an initial guess for the unknown poses, hence they are more suitable for setups in which such guess is not available (e.g., multi-robot localization, recovery after localization failure). We tested the proposed techniques in extensive simulations, and we show that some of the proposed relaxations are indeed tight (i.e., they solve the original nonconvex problem 10 exactly) and ensure accurate estimation in the face of a large number of outliers.

Convex Relaxations for Pose Graph Optimization with Outliers

http://arxiv.org/pdf/1801.02112v1

Title Convex Relaxations Pose Graph Optimization Outliers Summary Pose Graph Optimization involves estimation set pose pairwise measurement provides formalization many problem arising mobile robotics geometric computer vision paper consider case subset measurement fed pose graph optimization spurious first contribution develop robust estimator cope heavytailed measurement noise hence increasing robustness presence outlier Since resulting estimator require solving nonconvex optimization problem develop convex relaxation approximately solve problem via semidefinite programming provide condition proposed relaxation exact Contrarily existing approach convex relaxation rely availability initial guess unknown pose hence suitable setup guess available eg multirobot localization recovery localization failure tested proposed technique extensive simulation show proposed relaxation indeed tight ie solve original nonconvex problem 10 exactly ensure accurate estimation face large number outlier Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mojtaba S. Fazli', 'Stephen A. Vella', 'Silvia N. J. Moreno', 'Shannon Quinn']

1801.02591v2

Toxoplasma gondii is a parasitic protozoan that causes dis- seminated toxoplasmosis, a disease that afflicts roughly a third of the worlds population. Its virulence is predicated on its motility and ability to enter and exit nucleated cells; therefore, studies elucidating its mechanism of motility and in particular, its motility patterns in the context of its lytic cycle, are critical to the eventual development of therapeutic strate- gies. Here, we present an end-to-end computational pipeline for identifying T. gondii motility phenotypes in a completely unsupervised, data-driven way. We track the parasites before and after addition of extracellular Ca2+ to study its effects on the parasite motility patterns and use this information to parameterize the motion and group it according to similarity of spatiotemporal dynamics.

Unsupervised Discovery of Toxoplasma gondii Motility Phenotypes

http://arxiv.org/pdf/1801.02591v2

Title Unsupervised Discovery Toxoplasma gondii Motility Phenotypes Summary Toxoplasma gondii parasitic protozoan cause dis seminated toxoplasmosis disease afflicts roughly third world population virulence predicated motility ability enter exit nucleated cell therefore study elucidating mechanism motility particular motility pattern context lytic cycle critical eventual development therapeutic strate gy present endtoend computational pipeline identifying gondii motility phenotype completely unsupervised datadriven way track parasite addition extracellular Ca2 study effect parasite motility pattern use information parameterize motion group according similarity spatiotemporal dynamic Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Lina Karam', 'Tejas Borkar', 'Yu Cao', 'Junseok Chae']

1801.02684v1

This paper introduces a deep learning enabled generative sensing framework which integrates low-end sensors with computational intelligence to attain a high recognition accuracy on par with that attained with high-end sensors. The proposed generative sensing framework aims at transforming low-end, low-quality sensor data into higher quality sensor data in terms of achieved classification accuracy. The low-end data can be transformed into higher quality data of the same modality or into data of another modality. Different from existing methods for image generation, the proposed framework is based on discriminative models and targets to maximize the recognition accuracy rather than a similarity measure. This is achieved through the introduction of selective feature regeneration in a deep neural network (DNN). The proposed generative sensing will essentially transform low-quality sensor data into high-quality information for robust perception. Results are presented to illustrate the performance of the proposed framework.

Generative Sensing: Transforming Unreliable Sensor Data for Reliable Recognition

http://arxiv.org/pdf/1801.02684v1

Title Generative Sensing Transforming Unreliable Sensor Data Reliable Recognition Summary paper introduces deep learning enabled generative sensing framework integrates lowend sensor computational intelligence attain high recognition accuracy par attained highend sensor proposed generative sensing framework aim transforming lowend lowquality sensor data higher quality sensor data term achieved classification accuracy lowend data transformed higher quality data modality data another modality Different existing method image generation proposed framework based discriminative model target maximize recognition accuracy rather similarity measure achieved introduction selective feature regeneration deep neural network DNN proposed generative sensing essentially transform lowquality sensor data highquality information robust perception Results presented illustrate performance proposed framework Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Achim Kampker', 'Mohsen Sefati', 'Arya Abdul Rachman', 'Kai Kreisköther', 'Pascual Campoy']

1801.02686v2

Urban-oriented autonomous vehicles require a reliable perception technology to tackle the high amount of uncertainties. The recently introduced compact 3D LIDAR sensor offers a surround spatial information that can be exploited to enhance the vehicle perception. We present a real-time integrated framework of multi-target object detection and tracking using 3D LIDAR geared toward urban use. Our approach combines sensor occlusion-aware detection method with computationally efficient heuristics rule-based filtering and adaptive probabilistic tracking to handle uncertainties arising from sensing limitation of 3D LIDAR and complexity of the target object movement. The evaluation results using real-world pre-recorded 3D LIDAR data and comparison with state-of-the-art works shows that our framework is capable of achieving promising tracking performance in the urban situation.

Towards Multi-Object Detection and Tracking in Urban Scenario under Uncertainties

http://arxiv.org/pdf/1801.02686v2

Title Towards MultiObject Detection Tracking Urban Scenario Uncertainties Summary Urbanoriented autonomous vehicle require reliable perception technology tackle high amount uncertainty recently introduced compact 3D LIDAR sensor offer surround spatial information exploited enhance vehicle perception present realtime integrated framework multitarget object detection tracking using 3D LIDAR geared toward urban use approach combine sensor occlusionaware detection method computationally efficient heuristic rulebased filtering adaptive probabilistic tracking handle uncertainty arising sensing limitation 3D LIDAR complexity target object movement evaluation result using realworld prerecorded 3D LIDAR data comparison stateoftheart work show framework capable achieving promising tracking performance urban situation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yuhua Chen', 'Yibin Xie', 'Zhengwei Zhou', 'Feng Shi', 'Anthony G. Christodoulou', 'Debiao Li']

1801.02728v1

Magnetic resonance image (MRI) in high spatial resolution provides detailed anatomical information and is often necessary for accurate quantitative analysis. However, high spatial resolution typically comes at the expense of longer scan time, less spatial coverage, and lower signal to noise ratio (SNR). Single Image Super-Resolution (SISR), a technique aimed to restore high-resolution (HR) details from one single low-resolution (LR) input image, has been improved dramatically by recent breakthroughs in deep learning. In this paper, we introduce a new neural network architecture, 3D Densely Connected Super-Resolution Networks (DCSRN) to restore HR features of structural brain MR images. Through experiments on a dataset with 1,113 subjects, we demonstrate that our network outperforms bicubic interpolation as well as other deep learning methods in restoring 4x resolution-reduced images.

Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks

http://arxiv.org/pdf/1801.02728v1

Title Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks Summary Magnetic resonance image MRI high spatial resolution provides detailed anatomical information often necessary accurate quantitative analysis However high spatial resolution typically come expense longer scan time le spatial coverage lower signal noise ratio SNR Single Image SuperResolution SISR technique aimed restore highresolution HR detail one single lowresolution LR input image improved dramatically recent breakthrough deep learning paper introduce new neural network architecture 3D Densely Connected SuperResolution Networks DCSRN restore HR feature structural brain MR image experiment dataset 1113 subject demonstrate network outperforms bicubic interpolation well deep learning method restoring 4x resolutionreduced 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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['Mario Michael Krell', 'Anett Seeland', 'Su Kyoung Kim']

1801.02730v1

On image data, data augmentation is becoming less relevant due to the large amount of available training data and regularization techniques. Common approaches are moving windows (cropping), scaling, affine distortions, random noise, and elastic deformations. For electroencephalographic data, the lack of sufficient training data is still a major issue. We suggest and evaluate different approaches to generate augmented data using temporal and spatial/rotational distortions. Our results on the perception of rare stimuli (P300 data) and movement prediction (MRCP data) show that these approaches are feasible and can significantly increase the performance of signal processing chains for brain-computer interfaces by 1% to 6%.

Data Augmentation for Brain-Computer Interfaces: Analysis on Event-Related Potentials Data

http://arxiv.org/pdf/1801.02730v1

Title Data Augmentation BrainComputer Interfaces Analysis EventRelated Potentials Data Summary image data data augmentation becoming le relevant due large amount available training data regularization technique Common approach moving window cropping scaling affine distortion random noise elastic deformation electroencephalographic data lack sufficient training data still major issue suggest evaluate different approach generate augmented data using temporal spatialrotational distortion result perception rare stimulus P300 data movement prediction MRCP data show approach feasible significantly increase performance signal processing chain braincomputer interface 1 6 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ugo Boscain', 'Roman Chertovskih', 'Jean-Paul Gauthier', 'Dario Prandi', 'Alexey Remizov']

1801.03800v1

In this paper we review several algorithms for image inpainting based on the hypoelliptic diffusion naturally associated with a mathematical model of the primary visual cortex. In particular, we present one algorithm that does not exploit the information of where the image is corrupted, and others that do it. While the first algorithm is able to reconstruct only images that our visual system is still capable of recognize, we show that those of the second type completely transcend such limitation providing reconstructions at the state-of-the-art in image inpainting. This can be interpreted as a validation of the fact that our visual cortex actually encodes the first type of algorithm.

Cortical-inspired image reconstruction via sub-Riemannian geometry and hypoelliptic diffusion

http://arxiv.org/pdf/1801.03800v1

Title Corticalinspired image reconstruction via subRiemannian geometry hypoelliptic diffusion Summary paper review several algorithm image inpainting based hypoelliptic diffusion naturally associated mathematical model primary visual cortex particular present one algorithm exploit information image corrupted others first algorithm able reconstruct image visual system still capable recognize show second type completely transcend limitation providing reconstruction stateoftheart image inpainting interpreted validation fact visual cortex actually encodes first type algorithm Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Richard Zhang', 'Phillip Isola', 'Alexei A. Efros', 'Eli Shechtman', 'Oliver Wang']

1801.03924v1

While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on the ImageNet classification task has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called "perceptual losses"? What elements are critical for their success? To answer these questions, we introduce a new Full Reference Image Quality Assessment (FR-IQA) dataset of perceptual human judgments, orders of magnitude larger than previous datasets. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by huge margins. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations.

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

http://arxiv.org/pdf/1801.03924v1

Title Unreasonable Effectiveness Deep Features Perceptual Metric Summary nearly effortless human quickly ass perceptual similarity two image underlying process thought quite complex Despite widely used perceptual metric today PSNR SSIM simple shallow function fail account many nuance human perception Recently deep learning community found feature VGG network trained ImageNet classification task remarkably useful training loss image synthesis perceptual socalled perceptual loss element critical success answer question introduce new Full Reference Image Quality Assessment FRIQA dataset perceptual human judgment order magnitude larger previous datasets systematically evaluate deep feature across different architecture task compare classic metric find deep feature outperform previous metric huge margin surprisingly result restricted ImageNettrained VGG feature hold across different deep architecture level supervision supervised selfsupervised even unsupervised result suggest perceptual similarity emergent property shared across deep visual 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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['Cameron Fabbri', 'Md Jahidul Islam', 'Junaed Sattar']

1801.04011v1

Autonomous underwater vehicles (AUVs) rely on a variety of sensors - acoustic, inertial and visual - for intelligent decision making. Due to its non-intrusive, passive nature, and high information content, vision is an attractive sensing modality, particularly at shallower depths. However, factors such as light refraction and absorption, suspended particles in the water, and color distortion affect the quality of visual data, resulting in noisy and distorted images. AUVs that rely on visual sensing thus face difficult challenges, and consequently exhibit poor performance on vision-driven tasks. This paper proposes a method to improve the quality of visual underwater scenes using Generative Adversarial Networks (GANs), with the goal of improving input to vision-driven behaviors further down the autonomy pipeline. Furthermore, we show how recently proposed methods are able to generate a dataset for the purpose of such underwater image restoration. For any visually-guided underwater robots, this improvement can result in increased safety and reliability through robust visual perception. To that effect, we present quantitative and qualitative data which demonstrates that images corrected through the proposed approach generate more visually appealing images, and also provide increased accuracy for a diver tracking algorithm.

Enhancing Underwater Imagery using Generative Adversarial Networks

http://arxiv.org/pdf/1801.04011v1

Title Enhancing Underwater Imagery using Generative Adversarial Networks Summary Autonomous underwater vehicle AUVs rely variety sensor acoustic inertial visual intelligent decision making Due nonintrusive passive nature high information content vision attractive sensing modality particularly shallower depth However factor light refraction absorption suspended particle water color distortion affect quality visual data resulting noisy distorted image AUVs rely visual sensing thus face difficult challenge consequently exhibit poor performance visiondriven task paper proposes method improve quality visual underwater scene using Generative Adversarial Networks GANs goal improving input visiondriven behavior autonomy pipeline Furthermore show recently proposed method able generate dataset purpose underwater image restoration visuallyguided underwater robot improvement result increased safety reliability robust visual perception effect present quantitative qualitative data demonstrates image corrected proposed approach generate visually appealing image also provide increased accuracy diver tracking algorithm Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mehdi Yedroudj', 'Marc Chaumont', 'Frédéric Comby']

1801.04076v2

Deep learning and convolutional neural networks (CNN) have been intensively used in many image processing topics during last years. As far as steganalysis is concerned, the use of CNN allows reaching the state-of-the-art results. The performances of such networks often rely on the size of their learning database. An obvious preliminary assumption could be considering that "the bigger a database is, the better the results are". However, it appears that cautions have to be taken when increasing the database size if one desire to improve the classification accuracy i.e. enhance the steganalysis efficiency. To our knowledge, no study has been performed on the enrichment impact of a learning database on the steganalysis performance. What kind of images can be added to the initial learning set? What are the sensitive criteria: the camera models used for acquiring the images, the treatments applied to the images, the cameras proportions in the database, etc? This article continues the work carried out in a previous paper, and explores the ways to improve the performances of CNN. It aims at studying the effects of "base augmentation" on the performance of steganalysis using a CNN. We present the results of this study using various experimental protocols and various databases to define the good practices in base augmentation for steganalysis.

How to augment a small learning set for improving the performances of a CNN-based steganalyzer?

http://arxiv.org/pdf/1801.04076v2

Title augment small learning set improving performance CNNbased steganalyzer Summary Deep learning convolutional neural network CNN intensively used many image processing topic last year far steganalysis concerned use CNN allows reaching stateoftheart result performance network often rely size learning database obvious preliminary assumption could considering bigger database better result However appears caution taken increasing database size one desire improve classification accuracy ie enhance steganalysis efficiency knowledge study performed enrichment impact learning database steganalysis performance kind image added initial learning set sensitive criterion camera model used acquiring image treatment applied image camera proportion database etc article continues work carried previous paper explores way improve performance CNN aim studying effect base augmentation performance steganalysis using CNN present result study using various experimental protocol various database define good practice base augmentation steganalysis Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Kalthoum Ouerghi', 'Najib Fadlallah', 'Amor Smida', 'Ridha Ghayoula', 'Jaouhar Fattahi', 'Noureddine Boulejfen']

1801.05068v1

Microwave imaging for breast cancer detection is based on the contrast in the electrical properties of healthy fatty breast tissues. This paper presents an industrial, scientific and medical (ISM) bands comparative study of five microstrip patch antennas for microwave imaging at a frequency of 2.45 GHz. The choice of one antenna is made for an antenna array composed of 8 antennas for a microwave breast imaging system. Each antenna element is arranged in a circular configuration so that it can be directly faced to the breast phantom for better tumor detection. This choice is made by putting each antenna alone on the Breast skin to study the electric field, magnetic fields and current density in the healthy tissue of the breast phantom designed and simulated in Ansoft High Frequency Simulation Software (HFSS).

Circular Antenna Array Design for Breast Cancer Detection

http://arxiv.org/pdf/1801.05068v1

Title Circular Antenna Array Design Breast Cancer Detection Summary Microwave imaging breast cancer detection based contrast electrical property healthy fatty breast tissue paper present industrial scientific medical ISM band comparative study five microstrip patch antenna microwave imaging frequency 245 GHz choice one antenna made antenna array composed 8 antenna microwave breast imaging system antenna element arranged circular configuration directly faced breast phantom better tumor detection choice made putting antenna alone Breast skin study electric field magnetic field current density healthy tissue breast phantom designed simulated Ansoft High Frequency Simulation Software HFSS Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher 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', 'Per Cullhed', 'Ekta Vats']

1801.05367v1

This paper presents a framework for semi-automatic transcription of large-scale historical handwritten documents and proposes a simple user-friendly text extractor tool, TexT for transcription. The proposed approach provides a quick and easy transcription of text using computer assisted interactive technique. The algorithm finds multiple occurrences of the marked text on-the-fly using a word spotting system. TexT is also capable of performing on-the-fly annotation of handwritten text with automatic generation of ground truth labels, and dynamic adjustment and correction of user generated bounding box annotations with the word being perfectly encapsulated. The user can view the document and the found words in the original form or with background noise removed for easier visualization of transcription results. The effectiveness of TexT is demonstrated on an archival manuscript collection from well-known publicly available dataset.

TexT - Text Extractor Tool for Handwritten Document Transcription and Annotation

http://arxiv.org/pdf/1801.05367v1

Title TexT Text Extractor Tool Handwritten Document Transcription Annotation Summary paper present framework semiautomatic transcription largescale historical handwritten document proposes simple userfriendly text extractor tool TexT transcription proposed approach provides quick easy transcription text using computer assisted interactive technique algorithm find multiple occurrence marked text onthefly using word spotting system TexT also capable performing onthefly annotation handwritten text automatic generation ground truth label dynamic adjustment correction user generated bounding box annotation word perfectly encapsulated user view document found word original form background noise removed easier visualization transcription result effectiveness TexT demonstrated archival manuscript collection wellknown publicly available dataset Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Tiep Vu', 'Lam Nguyen', 'Tiantong Guo', 'Vishal Monga']

1801.05458v2

Classifying buried and obscured targets of interest from other natural and manmade clutter objects in the scene is an important problem for the U.S. Army. Targets of interest are often represented by signals captured using low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture radar (SAR) technology. This technology has been used in various applications, including ground penetration and sensing-through-the-wall. However, the technology still faces a significant issues regarding low-resolution SAR imagery in this particular frequency band, low radar cross sections (RCS), small objects compared to radar signal wavelengths, and heavy interference. The classification problem has been firstly, and partially, addressed by sparse representation-based classification (SRC) method which can extract noise from signals and exploit the cross-channel information. Despite providing potential results, SRC-related methods have drawbacks in representing nonlinear relations and dealing with larger training sets. In this paper, we propose a Simultaneous Decomposition and Classification Network (SDCN) to alleviate noise inferences and enhance classification accuracy. The network contains two jointly trained sub-networks: the decomposition sub-network handles denoising, while the classification sub-network discriminates targets from confusers. Experimental results show significant improvements over a network without decomposition and SRC-related methods.

Deep Network for Simultaneous Decomposition and Classification in UWB-SAR Imagery

http://arxiv.org/pdf/1801.05458v2

Title Deep Network Simultaneous Decomposition Classification UWBSAR Imagery Summary Classifying buried obscured target interest natural manmade clutter object scene important problem US Army Targets interest often represented signal captured using lowfrequency UHF Lband ultrawideband UWB synthetic aperture radar SAR technology technology used various application including ground penetration sensingthroughthewall However technology still face significant issue regarding lowresolution SAR imagery particular frequency band low radar cross section RCS small object compared radar signal wavelength heavy interference classification problem firstly partially addressed sparse representationbased classification SRC method extract noise signal exploit crosschannel information Despite providing potential result SRCrelated method drawback representing nonlinear relation dealing larger training set paper propose Simultaneous Decomposition Classification Network SDCN alleviate noise inference enhance classification accuracy network contains two jointly trained subnetworks decomposition subnetwork handle denoising classification subnetwork discriminates target confusers Experimental result show significant improvement network without decomposition SRCrelated 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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['Wuilan Torres', 'Antonio Rueda-Toicen']

1801.05525v1

The segmentation of satellite images is a necessary step to perform object-oriented image classification, which has become relevant due to its applicability on images with a high spatial resolution. To perform object-oriented image classification, the studied image must first be segmented in uniform regions. This segmentation requires manual work by an expert user, who must exhaustively explore the image to establish thresholds that generate useful and representative segments without oversegmenting and without discarding representative segments. We propose a technique that automatically segments the multispectral image while facing these issues. We identify in the image homogenous zones according to their spectral signatures through the use of morphological filters. These homogenous zones are representatives of different types of land coverings in the image and are used as seeds for the GrowCut multispectral segmentation algorithm. GrowCut is a cellular automaton with competitive region growth, its cells are linked to every pixel in the image through three parameters: the pixel's spectral signature, a label, and a strength factor that represents the strength with which a cell defends its label. The seed cells possess maximum strength and maintain their state throughout the automaton's evolution. Starting from seed cells, each cell in the image is iteratively attacked by its neighboring cells. When the automaton stops updating its states, we obtain a segmented image where each pixel has taken the label of one of its cells. In this paper the algorithm was applied in an image acquired by Landsat8 on agricultural land of Calabozo, Guarico, Venezuela where there are different types of land coverings: agriculture, urban regions, water bodies, and savannas with different degrees of human intervention. The segmentation obtained is presented as irregular polygons enclosing geographical objects.

Identification of Seed Cells in Multispectral Images for GrowCut Segmentation

http://arxiv.org/pdf/1801.05525v1

Title Identification Seed Cells Multispectral Images GrowCut Segmentation Summary segmentation satellite image necessary step perform objectoriented image classification become relevant due applicability image high spatial resolution perform objectoriented image classification studied image must first segmented uniform region segmentation requires manual work expert user must exhaustively explore image establish threshold generate useful representative segment without oversegmenting without discarding representative segment propose technique automatically segment multispectral image facing issue identify image homogenous zone according spectral signature use morphological filter homogenous zone representative different type land covering image used seed GrowCut multispectral segmentation algorithm GrowCut cellular automaton competitive region growth cell linked every pixel image three parameter pixel spectral signature label strength factor represents strength cell defends label seed cell posse maximum strength maintain state throughout automaton evolution Starting seed cell cell image iteratively attacked neighboring cell automaton stop updating state obtain segmented image pixel taken label one cell paper algorithm applied image acquired Landsat8 agricultural land Calabozo Guarico Venezuela different type land covering agriculture urban region water body savanna different degree human intervention segmentation obtained presented irregular polygon enclosing geographical object Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Harald Garcke', 'Kei Fong Lam', 'Vanessa Styles']

1801.05527v1

The inpainting of damaged images has a wide range of applications and many different mathematical methods have been proposed to solve this problem. Inpainting witht the help of Cahn--Hilliard models has been particularly successful, and it turns out that Cahn--Hilliard inpainting with the double obstacle potential can lead to better results compared to inpainting with a smooth double well potential. However, a mathematical analysis of this approach is missing so far. In this paper we give first analytical results for a Cahn--Hilliard double obstacle model and in particular we can show existence of stationary solutions without constraints on the parameters involved. With the help of numerical results we show the effectiveness of the approach for binary and grayscale images.

Cahn--Hilliard inpainting with the double obstacle potential

http://arxiv.org/pdf/1801.05527v1

Title CahnHilliard inpainting double obstacle potential Summary inpainting damaged image wide range application many different mathematical method proposed solve problem Inpainting witht help CahnHilliard model particularly successful turn CahnHilliard inpainting double obstacle potential lead better result compared inpainting smooth double well potential However mathematical analysis approach missing far paper give first analytical result CahnHilliard double obstacle model particular show existence stationary solution without constraint parameter involved help numerical result show effectiveness approach binary grayscale 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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['Joscha Diehl', 'Jeremy Reizenstein']

1801.06104v1

We introduce a novel class of features for multidimensional time series, that are invariant with respect to transformations of the ambient space. The general linear group, the group of rotations and the group of permutations of the axes are considered. The starting point for their construction is Chen's iterated-integral signature.

Invariants of multidimensional time series based on their iterated-integral signature

http://arxiv.org/pdf/1801.06104v1

Title Invariants multidimensional time series based iteratedintegral signature Summary introduce novel class feature multidimensional time series invariant respect transformation ambient space general linear group group rotation group permutation ax considered starting point construction Chens iteratedintegral signature Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Siyeong Lee', 'Gwon Hwan An', 'Suk-Ju Kang']

1801.06277v1

In this paper, we propose a novel deep neural network model that reconstructs a high dynamic range (HDR) image from a single low dynamic range (LDR) image. The proposed model is based on a convolutional neural network composed of dilated convolutional layers, and infers LDR images with various exposures and illumination from a single LDR image of the same scene. Then, the final HDR image can be formed by merging these inference results. It is relatively easy for the proposed method to find the mapping between the LDR and an HDR with a different bit depth because of the chaining structure inferring the relationship between the LDR images with brighter (or darker) exposures from a given LDR image. The method not only extends the range, but also has the advantage of restoring the light information of the actual physical world. For the HDR images obtained by the proposed method, the HDR-VDP2 Q score, which is the most popular evaluation metric for HDR images, was 56.36 for a display with a 1920$\times$1200 resolution, which is an improvement of 6 compared with the scores of conventional algorithms. In addition, when comparing the peak signal-to-noise ratio values for tone mapped HDR images generated by the proposed and conventional algorithms, the average value obtained by the proposed algorithm is 30.86 dB, which is 10 dB higher than those obtained by the conventional algorithms.

Deep Chain HDRI: Reconstructing a High Dynamic Range Image from a Single Low Dynamic Range Image

http://arxiv.org/pdf/1801.06277v1

Title Deep Chain HDRI Reconstructing High Dynamic Range Image Single Low Dynamic Range Image Summary paper propose novel deep neural network model reconstructs high dynamic range HDR image single low dynamic range LDR image proposed model based convolutional neural network composed dilated convolutional layer infers LDR image various exposure illumination single LDR image scene final HDR image formed merging inference result relatively easy proposed method find mapping LDR HDR different bit depth chaining structure inferring relationship LDR image brighter darker exposure given LDR image method extends range also advantage restoring light information actual physical world HDR image obtained proposed method HDRVDP2 Q score popular evaluation metric HDR image 5636 display 1920times1200 resolution improvement 6 compared score conventional algorithm addition comparing peak signaltonoise ratio value tone mapped HDR image generated proposed conventional algorithm average value obtained proposed algorithm 3086 dB 10 dB higher obtained conventional algorithm Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Penghang Yin', 'Shuai Zhang', 'Jiancheng Lyu', 'Stanley Osher', 'Yingyong Qi', 'Jack Xin']

1801.06313v1

We propose BinaryRelax, a simple two-phase algorithm, for training deep neural networks with quantized weights. The set constraint that characterizes the quantization of weights is not imposed until the late stage of training, and a sequence of pseudo quantized weights is maintained. Specifically, we relax the hard constraint into a continuous regularizer via Moreau envelope, which turns out to be the squared Euclidean distance to the set of quantized weights. The pseudo quantized weights are obtained by linearly interpolating between the float weights and their quantizations. A continuation strategy is adopted to push the weights towards the quantized state by gradually increasing the regularization parameter. In the second phase, exact quantization scheme with a small learning rate is invoked to guarantee fully quantized weights. We test BinaryRelax on the benchmark CIFAR-10 and CIFAR-100 color image datasets to demonstrate the superiority of the relaxed quantization approach and the improved accuracy over the state-of-the-art training methods. Finally, we prove the convergence of BinaryRelax under an approximate orthogonality condition.

BinaryRelax: A Relaxation Approach For Training Deep Neural Networks With Quantized Weights

http://arxiv.org/pdf/1801.06313v1

Title BinaryRelax Relaxation Approach Training Deep Neural Networks Quantized Weights Summary propose BinaryRelax simple twophase algorithm training deep neural network quantized weight set constraint characterizes quantization weight imposed late stage training sequence pseudo quantized weight maintained Specifically relax hard constraint continuous regularizer via Moreau envelope turn squared Euclidean distance set quantized weight pseudo quantized weight obtained linearly interpolating float weight quantization continuation strategy adopted push weight towards quantized state gradually increasing regularization parameter second phase exact quantization scheme small learning rate invoked guarantee fully quantized weight test BinaryRelax benchmark CIFAR10 CIFAR100 color image datasets demonstrate superiority relaxed quantization approach improved accuracy stateoftheart training method Finally prove convergence BinaryRelax approximate orthogonality condition Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Daniel Moreira', 'Aparna Bharati', 'Joel Brogan', 'Allan Pinto', 'Michael Parowski', 'Kevin W. Bowyer', 'Patrick J. Flynn', 'Anderson Rocha', 'Walter J. Scheirer']

1801.06510v2

Prior art has shown it is possible to estimate, through image processing and computer vision techniques, the types and parameters of transformations that have been applied to the content of individual images to obtain new images. Given a large corpus of images and a query image, an interesting further step is to retrieve the set of original images whose content is present in the query image, as well as the detailed sequences of transformations that yield the query image given the original images. This is a problem that recently has received the name of image provenance analysis. In these times of public media manipulation ( e.g., fake news and meme sharing), obtaining the history of image transformations is relevant for fact checking and authorship verification, among many other applications. This article presents an end-to-end processing pipeline for image provenance analysis, which works at real-world scale. It employs a cutting-edge image filtering solution that is custom-tailored for the problem at hand, as well as novel techniques for obtaining the provenance graph that expresses how the images, as nodes, are ancestrally connected. A comprehensive set of experiments for each stage of the pipeline is provided, comparing the proposed solution with state-of-the-art results, employing previously published datasets. In addition, this work introduces a new dataset of real-world provenance cases from the social media site Reddit, along with baseline results.

Image Provenance Analysis at Scale

http://arxiv.org/pdf/1801.06510v2

Title Image Provenance Analysis Scale Summary Prior art shown possible estimate image processing computer vision technique type parameter transformation applied content individual image obtain new image Given large corpus image query image interesting step retrieve set original image whose content present query image well detailed sequence transformation yield query image given original image problem recently received name image provenance analysis time public medium manipulation eg fake news meme sharing obtaining history image transformation relevant fact checking authorship verification among many application article present endtoend processing pipeline image provenance analysis work realworld scale employ cuttingedge image filtering solution customtailored problem hand well novel technique obtaining provenance graph express image node ancestrally connected comprehensive set experiment stage pipeline provided comparing proposed solution stateoftheart result employing previously published datasets addition work introduces new dataset realworld provenance case social medium site Reddit along baseline result Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Lijun Zhao', 'Huihui Bai', 'Anhong Wang', 'Yao Zhao']

1801.06611v1

Multiple description coding (MDC) is able to stably transmit the signal in the un-reliable and non-prioritized networks, which has been broadly studied for several decades. However, the traditional MDC doesn't well leverage image's context features to generate multiple descriptions. In this paper, we propose a novel standard-compliant convolutional neural network-based MDC framework in term of image's context features. Firstly, multiple description generator network (MDGN) is designed to produce appearance-similar yet feature-different multiple descriptions automatically according to image's content, which are compressed by standard codec. Secondly, we present multiple description reconstruction network (MDRN) including side reconstruction network (SRN) and central reconstruction network (CRN). When any one of two lossy descriptions is received at the decoder, SRN network is used to improve the quality of this decoded lossy description by removing the compression artifact and up-sampling simultaneously. Meanwhile, we utilize CRN network with two decoded descriptions as inputs for better reconstruction, if both of lossy descriptions are available. Thirdly, multiple description virtual codec network (MDVCN) is proposed to bridge the gap between MDGN network and MDRN network in order to train an end-to-end MDC framework. Here, two learning algorithms are provided to train our whole framework. In addition to structural similarity loss function, the produced descriptions are used as opposing labels with multiple description distance loss function to regularize the training of MDGN network. These losses guarantee that the generated description images are structurally similar yet finely diverse. Experimental results show a great deal of objective and subjective quality measurements to validate the efficiency of the proposed method.

Multiple Description Convolutional Neural Networks for Image Compression

http://arxiv.org/pdf/1801.06611v1

Title Multiple Description Convolutional Neural Networks Image Compression Summary Multiple description coding MDC able stably transmit signal unreliable nonprioritized network broadly studied several decade However traditional MDC doesnt well leverage image context feature generate multiple description paper propose novel standardcompliant convolutional neural networkbased MDC framework term image context feature Firstly multiple description generator network MDGN designed produce appearancesimilar yet featuredifferent multiple description automatically according image content compressed standard codec Secondly present multiple description reconstruction network MDRN including side reconstruction network SRN central reconstruction network CRN one two lossy description received decoder SRN network used improve quality decoded lossy description removing compression artifact upsampling simultaneously Meanwhile utilize CRN network two decoded description input better reconstruction lossy description available Thirdly multiple description virtual codec network MDVCN proposed bridge gap MDGN network MDRN network order train endtoend MDC framework two learning algorithm provided train whole framework addition structural similarity loss function produced description used opposing label multiple description distance loss function regularize training MDGN network loss guarantee generated description image structurally similar yet finely diverse Experimental result show great deal objective subjective quality measurement validate efficiency 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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['Alejandro Cartas', 'María Elena Algorri']

1801.06694v1

We present a new method for the recognition of digital straight lines based on the slope. This method combines the Freeman's chain coding scheme and new discovered properties of the digital slope introduced in this paper. We also present the efficiency of our method from a testbed.

Determination of Digital Straight Segments Using the Slope

http://arxiv.org/pdf/1801.06694v1

Title Determination Digital Straight Segments Using Slope Summary present new method recognition digital straight line based slope method combine Freemans chain coding scheme new discovered property digital slope introduced paper also present efficiency method testbed Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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