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['Eric Cristofalo', 'Zijian Wang']

1705.01152v1

In this project, we propose a novel approach for estimating depth from RGB images. Traditionally, most work uses a single RGB image to estimate depth, which is inherently difficult and generally results in poor performance, even with thousands of data examples. In this work, we alternatively use multiple RGB images that were captured while changing the focus of the camera's lens. This method leverages the natural depth information correlated to the different patterns of clarity/blur in the sequence of focal images, which helps distinguish objects at different depths. Since no such data set exists for learning this mapping, we collect our own data set using customized hardware. We then use a convolutional neural network for learning the depth from the stacked focal images. Comparative studies were conducted on both a standard RGBD data set and our own data set (learning from both single and multiple images), and results verified that stacked focal images yield better depth estimation than using just single RGB image.

Out-of-focus: Learning Depth from Image Bokeh for Robotic Perception

http://arxiv.org/pdf/1705.01152v1

Title Outoffocus Learning Depth Image Bokeh Robotic Perception Summary project propose novel approach estimating depth RGB image Traditionally work us single RGB image estimate depth inherently difficult generally result poor performance even thousand data example work alternatively use multiple RGB image captured changing focus camera lens method leverage natural depth information correlated different pattern clarityblur sequence focal image help distinguish object different depth Since data set exists learning mapping collect data set using customized hardware use convolutional neural network learning depth stacked focal image Comparative study conducted standard RGBD data set data set learning single multiple image result verified stacked focal image yield better depth estimation using single RGB 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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['Balazs Kovacs', 'Sean Bell', 'Noah Snavely', 'Kavita Bala']

1705.01156v1

Understanding shading effects in images is critical for a variety of vision and graphics problems, including intrinsic image decomposition, shadow removal, image relighting, and inverse rendering. As is the case with other vision tasks, machine learning is a promising approach to understanding shading - but there is little ground truth shading data available for real-world images. We introduce Shading Annotations in the Wild (SAW), a new large-scale, public dataset of shading annotations in indoor scenes, comprised of multiple forms of shading judgments obtained via crowdsourcing, along with shading annotations automatically generated from RGB-D imagery. We use this data to train a convolutional neural network to predict per-pixel shading information in an image. We demonstrate the value of our data and network in an application to intrinsic images, where we can reduce decomposition artifacts produced by existing algorithms. Our database is available at http://opensurfaces.cs.cornell.edu/saw/.

Shading Annotations in the Wild

http://arxiv.org/pdf/1705.01156v1

Title Shading Annotations Wild Summary Understanding shading effect image critical variety vision graphic problem including intrinsic image decomposition shadow removal image relighting inverse rendering case vision task machine learning promising approach understanding shading little ground truth shading data available realworld image introduce Shading Annotations Wild SAW new largescale public dataset shading annotation indoor scene comprised multiple form shading judgment obtained via crowdsourcing along shading annotation automatically generated RGBD imagery use data train convolutional neural network predict perpixel shading information image demonstrate value data network application intrinsic image reduce decomposition artifact produced existing algorithm database available httpopensurfacescscornelledusaw Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Dushyant Mehta', 'Srinath Sridhar', 'Oleksandr Sotnychenko', 'Helge Rhodin', 'Mohammad Shafiei', 'Hans-Peter Seidel', 'Weipeng Xu', 'Dan Casas', 'Christian Theobalt']

1705.01583v1

We present the first real-time method to capture the full global 3D skeletal pose of a human in a stable, temporally consistent manner using a single RGB camera. Our method combines a new convolutional neural network (CNN) based pose regressor with kinematic skeleton fitting. Our novel fully-convolutional pose formulation regresses 2D and 3D joint positions jointly in real time and does not require tightly cropped input frames. A real-time kinematic skeleton fitting method uses the CNN output to yield temporally stable 3D global pose reconstructions on the basis of a coherent kinematic skeleton. This makes our approach the first monocular RGB method usable in real-time applications such as 3D character control---thus far, the only monocular methods for such applications employed specialized RGB-D cameras. Our method's accuracy is quantitatively on par with the best offline 3D monocular RGB pose estimation methods. Our results are qualitatively comparable to, and sometimes better than, results from monocular RGB-D approaches, such as the Kinect. However, we show that our approach is more broadly applicable than RGB-D solutions, i.e. it works for outdoor scenes, community videos, and low quality commodity RGB cameras.

VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera

http://arxiv.org/pdf/1705.01583v1

Title VNect Realtime 3D Human Pose Estimation Single RGB Camera Summary present first realtime method capture full global 3D skeletal pose human stable temporally consistent manner using single RGB camera method combine new convolutional neural network CNN based pose regressor kinematic skeleton fitting novel fullyconvolutional pose formulation regress 2D 3D joint position jointly real time require tightly cropped input frame realtime kinematic skeleton fitting method us CNN output yield temporally stable 3D global pose reconstruction basis coherent kinematic skeleton make approach first monocular RGB method usable realtime application 3D character controlthus far monocular method application employed specialized RGBD camera method accuracy quantitatively par best offline 3D monocular RGB pose estimation method result qualitatively comparable sometimes better result monocular RGBD approach Kinect However show approach broadly applicable RGBD solution ie work outdoor scene community video low quality commodity RGB camera Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Teresa Heiss', 'Hubert Wagner']

1705.02045v2

We present an efficient algorithm to compute Euler characteristic curves of gray scale images of arbitrary dimension. In various applications the Euler characteristic curve is used as a descriptor of an image. Our algorithm is the first streaming algorithm for Euler characteristic curves. The usage of streaming removes the necessity to store the entire image in RAM. Experiments show that our implementation handles terabyte scale images on commodity hardware. Due to lock-free parallelism, it scales well with the number of processor cores. Additionally, we put the concept of the Euler characteristic curve in the wider context of computational topology. In particular, we explain the connection with persistence diagrams.

Streaming Algorithm for Euler Characteristic Curves of Multidimensional Images

http://arxiv.org/pdf/1705.02045v2

Title Streaming Algorithm Euler Characteristic Curves Multidimensional Images Summary present efficient algorithm compute Euler characteristic curve gray scale image arbitrary dimension various application Euler characteristic curve used descriptor image algorithm first streaming algorithm Euler characteristic curve usage streaming remove necessity store entire image RAM Experiments show implementation handle terabyte scale image commodity hardware Due lockfree parallelism scale well number processor core Additionally put concept Euler characteristic curve wider context computational topology particular explain connection persistence diagram Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jun Li', 'Kai Xu', 'Siddhartha Chaudhuri', 'Ersin Yumer', 'Hao Zhang', 'Leonidas Guibas']

1705.02090v2

We introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures. Our key insight is that 3D shapes are effectively characterized by their hierarchical organization of parts, which reflects fundamental intra-shape relationships such as adjacency and symmetry. We develop a recursive neural net (RvNN) based autoencoder to map a flat, unlabeled, arbitrary part layout to a compact code. The code effectively captures hierarchical structures of man-made 3D objects of varying structural complexities despite being fixed-dimensional: an associated decoder maps a code back to a full hierarchy. The learned bidirectional mapping is further tuned using an adversarial setup to yield a generative model of plausible structures, from which novel structures can be sampled. Finally, our structure synthesis framework is augmented by a second trained module that produces fine-grained part geometry, conditioned on global and local structural context, leading to a full generative pipeline for 3D shapes. We demonstrate that without supervision, our network learns meaningful structural hierarchies adhering to perceptual grouping principles, produces compact codes which enable applications such as shape classification and partial matching, and supports shape synthesis and interpolation with significant variations in topology and geometry.

GRASS: Generative Recursive Autoencoders for Shape Structures

http://arxiv.org/pdf/1705.02090v2

Title GRASS Generative Recursive Autoencoders Shape Structures Summary introduce novel neural network architecture encoding synthesis 3D shape particularly structure key insight 3D shape effectively characterized hierarchical organization part reflects fundamental intrashape relationship adjacency symmetry develop recursive neural net RvNN based autoencoder map flat unlabeled arbitrary part layout compact code code effectively capture hierarchical structure manmade 3D object varying structural complexity despite fixeddimensional associated decoder map code back full hierarchy learned bidirectional mapping tuned using adversarial setup yield generative model plausible structure novel structure sampled Finally structure synthesis framework augmented second trained module produce finegrained part geometry conditioned global local structural context leading full generative pipeline 3D shape demonstrate without supervision network learns meaningful structural hierarchy adhering perceptual grouping principle produce compact code enable application shape classification partial matching support shape synthesis interpolation significant variation topology geometry Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Seyed Mohammad Mahdi Alavi', 'Yunyan Zhang']

1705.02102v1

Following the presentation and proof of the hypothesis that image features are particularly perceived at points where the Fourier components are maximally in phase, the concept of phase congruency (PC) is introduced. Subsequently, a two-dimensional multi-scale phase congruency (2D-MSPC) is developed, which has been an important tool for detecting and evaluation of image features. However, the 2D-MSPC requires many parameters to be appropriately tuned for optimal image features detection. In this paper, we defined a criterion for parameter optimization of the 2D-MSPC, which is a function of its maximum and minimum moments. We formulated the problem in various optimal and suboptimal frameworks, and discussed the conditions and features of the suboptimal solutions. The effectiveness of the proposed method was verified through several examples, ranging from natural objects to medical images from patients with a neurological disease, multiple sclerosis.

Phase Congruency Parameter Optimization for Enhanced Detection of Image Features for both Natural and Medical Applications

http://arxiv.org/pdf/1705.02102v1

Title Phase Congruency Parameter Optimization Enhanced Detection Image Features Natural Medical Applications Summary Following presentation proof hypothesis image feature particularly perceived point Fourier component maximally phase concept phase congruency PC introduced Subsequently twodimensional multiscale phase congruency 2DMSPC developed important tool detecting evaluation image feature However 2DMSPC requires many parameter appropriately tuned optimal image feature detection paper defined criterion parameter optimization 2DMSPC function maximum minimum moment formulated problem various optimal suboptimal framework discussed condition feature suboptimal solution effectiveness proposed method verified several example ranging natural object medical image patient neurological disease multiple sclerosis Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Nilaksh Das', 'Madhuri Shanbhogue', 'Shang-Tse Chen', 'Fred Hohman', 'Li Chen', 'Michael E. Kounavis', 'Duen Horng Chau']

1705.02900v1

Deep neural networks (DNNs) have achieved great success in solving a variety of machine learning (ML) problems, especially in the domain of image recognition. However, recent research showed that DNNs can be highly vulnerable to adversarially generated instances, which look seemingly normal to human observers, but completely confuse DNNs. These adversarial samples are crafted by adding small perturbations to normal, benign images. Such perturbations, while imperceptible to the human eye, are picked up by DNNs and cause them to misclassify the manipulated instances with high confidence. In this work, we explore and demonstrate how systematic JPEG compression can work as an effective pre-processing step in the classification pipeline to counter adversarial attacks and dramatically reduce their effects (e.g., Fast Gradient Sign Method, DeepFool). An important component of JPEG compression is its ability to remove high frequency signal components, inside square blocks of an image. Such an operation is equivalent to selective blurring of the image, helping remove additive perturbations. Further, we propose an ensemble-based technique that can be constructed quickly from a given well-performing DNN, and empirically show how such an ensemble that leverages JPEG compression can protect a model from multiple types of adversarial attacks, without requiring knowledge about the model.

Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression

http://arxiv.org/pdf/1705.02900v1

Title Keeping Bad Guys Protecting Vaccinating Deep Learning JPEG Compression Summary Deep neural network DNNs achieved great success solving variety machine learning ML problem especially domain image recognition However recent research showed DNNs highly vulnerable adversarially generated instance look seemingly normal human observer completely confuse DNNs adversarial sample crafted adding small perturbation normal benign image perturbation imperceptible human eye picked DNNs cause misclassify manipulated instance high confidence work explore demonstrate systematic JPEG compression work effective preprocessing step classification pipeline counter adversarial attack dramatically reduce effect eg Fast Gradient Sign Method DeepFool important component JPEG compression ability remove high frequency signal component inside square block image operation equivalent selective blurring image helping remove additive perturbation propose ensemblebased technique constructed quickly given wellperforming DNN empirically show ensemble leverage JPEG compression protect model multiple type adversarial attack without requiring knowledge model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Rüdiger Alshut']

1705.02962v1

With image-based high-throughput experiments, new challenges arise in both, the design of experiments and the automated analysis. To be able to handle the massive number of single experiments and the corresponding amount of data, a comprehensive concept for the design of experiments and a new evaluation method is needed. This work proposes a new method for an optimized experiment layout that enables the determination of parameters, adapted for the needs of automated image analysis. Furthermore, a catalogue of new image analysis modules, especially developed for zebrafish analysis, is presented. The combination of both parts offers the user, usually a biologist, an approach for high-throughput zebrafish image analysis, which enables the extraction of new signals and optimizes the design of experiments. The result is a reduction of data amount, redundant information and workload as well as classification errors.

Konzept für Bildanalysen in Hochdurchsatz-Systemen am Beispiel des Zebrabärblings

http://arxiv.org/pdf/1705.02962v1

Title Konzept für Bildanalysen HochdurchsatzSystemen Beispiel de Zebrabärblings Summary imagebased highthroughput experiment new challenge arise design experiment automated analysis able handle massive number single experiment corresponding amount data comprehensive concept design experiment new evaluation method needed work proposes new method optimized experiment layout enables determination parameter adapted need automated image analysis Furthermore catalogue new image analysis module especially developed zebrafish analysis presented combination part offer user usually biologist approach highthroughput zebrafish image analysis enables extraction new signal optimizes design experiment result reduction data amount redundant information workload well classification error Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ting-Chun Wang', 'Jun-Yan Zhu', 'Nima Khademi Kalantari', 'Alexei A. Efros', 'Ravi Ramamoorthi']

1705.02997v1

Light field cameras have many advantages over traditional cameras, as they allow the user to change various camera settings after capture. However, capturing light fields requires a huge bandwidth to record the data: a modern light field camera can only take three images per second. This prevents current consumer light field cameras from capturing light field videos. Temporal interpolation at such extreme scale (10x, from 3 fps to 30 fps) is infeasible as too much information will be entirely missing between adjacent frames. Instead, we develop a hybrid imaging system, adding another standard video camera to capture the temporal information. Given a 3 fps light field sequence and a standard 30 fps 2D video, our system can then generate a full light field video at 30 fps. We adopt a learning-based approach, which can be decomposed into two steps: spatio-temporal flow estimation and appearance estimation. The flow estimation propagates the angular information from the light field sequence to the 2D video, so we can warp input images to the target view. The appearance estimation then combines these warped images to output the final pixels. The whole process is trained end-to-end using convolutional neural networks. Experimental results demonstrate that our algorithm outperforms current video interpolation methods, enabling consumer light field videography, and making applications such as refocusing and parallax view generation achievable on videos for the first time.

Light Field Video Capture Using a Learning-Based Hybrid Imaging System

http://arxiv.org/pdf/1705.02997v1

Title Light Field Video Capture Using LearningBased Hybrid Imaging System Summary Light field camera many advantage traditional camera allow user change various camera setting capture However capturing light field requires huge bandwidth record data modern light field camera take three image per second prevents current consumer light field camera capturing light field video Temporal interpolation extreme scale 10x 3 fps 30 fps infeasible much information entirely missing adjacent frame Instead develop hybrid imaging system adding another standard video camera capture temporal information Given 3 fps light field sequence standard 30 fps 2D video system generate full light field video 30 fps adopt learningbased approach decomposed two step spatiotemporal flow estimation appearance estimation flow estimation propagates angular information light field sequence 2D video warp input image target view appearance estimation combine warped image output final pixel whole process trained endtoend using convolutional neural network Experimental result demonstrate algorithm outperforms current video interpolation method enabling consumer light field videography making application refocusing parallax view generation achievable video first time Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Richard Zhang', 'Jun-Yan Zhu', 'Phillip Isola', 'Xinyang Geng', 'Angela S. Lin', 'Tianhe Yu', 'Alexei A. Efros']

1705.02999v1

We propose a deep learning approach for user-guided image colorization. The system directly maps a grayscale image, along with sparse, local user "hints" to an output colorization with a Convolutional Neural Network (CNN). Rather than using hand-defined rules, the network propagates user edits by fusing low-level cues along with high-level semantic information, learned from large-scale data. We train on a million images, with simulated user inputs. To guide the user towards efficient input selection, the system recommends likely colors based on the input image and current user inputs. The colorization is performed in a single feed-forward pass, enabling real-time use. Even with randomly simulated user inputs, we show that the proposed system helps novice users quickly create realistic colorizations, and offers large improvements in colorization quality with just a minute of use. In addition, we demonstrate that the framework can incorporate other user "hints" to the desired colorization, showing an application to color histogram transfer. Our code and models are available at https://richzhang.github.io/ideepcolor.

Real-Time User-Guided Image Colorization with Learned Deep Priors

http://arxiv.org/pdf/1705.02999v1

Title RealTime UserGuided Image Colorization Learned Deep Priors Summary propose deep learning approach userguided image colorization system directly map grayscale image along sparse local user hint output colorization Convolutional Neural Network CNN Rather using handdefined rule network propagates user edits fusing lowlevel cue along highlevel semantic information learned largescale data train million image simulated user input guide user towards efficient input selection system recommends likely color based input image current user input colorization performed single feedforward pas enabling realtime use Even randomly simulated user input show proposed system help novice user quickly create realistic colorizations offer large improvement colorization quality minute use addition demonstrate framework incorporate user hint desired colorization showing application color histogram transfer code model available httpsrichzhanggithubioideepcolor Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Carlos Guindel', 'Jorge Beltrán', 'David Martín', 'Fernando García']

1705.04085v3

Sensor setups consisting of a combination of 3D range scanner lasers and stereo vision systems are becoming a popular choice for on-board perception systems in vehicles; however, the combined use of both sources of information implies a tedious calibration process. We present a method for extrinsic calibration of lidar-stereo camera pairs without user intervention. Our calibration approach is aimed to cope with the constraints commonly found in automotive setups, such as low-resolution and specific sensor poses. To demonstrate the performance of our method, we also introduce a novel approach for the quantitative assessment of the calibration results, based on a simulation environment. Tests using real devices have been conducted as well, proving the usability of the system and the improvement over the existing approaches. Code is available at http://wiki.ros.org/velo2cam_calibration

Automatic Extrinsic Calibration for Lidar-Stereo Vehicle Sensor Setups

http://arxiv.org/pdf/1705.04085v3

Title Automatic Extrinsic Calibration LidarStereo Vehicle Sensor Setups Summary Sensor setup consisting combination 3D range scanner laser stereo vision system becoming popular choice onboard perception system vehicle however combined use source information implies tedious calibration process present method extrinsic calibration lidarstereo camera pair without user intervention calibration approach aimed cope constraint commonly found automotive setup lowresolution specific sensor pose demonstrate performance method also introduce novel approach quantitative assessment calibration result based simulation environment Tests using real device conducted well proving usability system improvement existing approach Code available httpwikirosorgvelo2camcalibration Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Brendt Wohlberg']

1705.04407v2

While convolutional sparse representations enjoy a number of useful properties, they have received limited attention for image reconstruction problems. The present paper compares the performance of block-based and convolutional sparse representations in the removal of Gaussian white noise. While the usual formulation of the convolutional sparse coding problem is slightly inferior to the block-based representations in this problem, the performance of the convolutional form can be boosted beyond that of the block-based form by the inclusion of suitable penalties on the gradients of the coefficient maps.

Convolutional Sparse Representations with Gradient Penalties

http://arxiv.org/pdf/1705.04407v2

Title Convolutional Sparse Representations Gradient Penalties Summary convolutional sparse representation enjoy number useful property received limited attention image reconstruction problem present paper compare performance blockbased convolutional sparse representation removal Gaussian white noise usual formulation convolutional sparse coding problem slightly inferior blockbased representation problem performance convolutional form boosted beyond blockbased form inclusion suitable penalty gradient coefficient 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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['Yao Yao', 'Jialv He', 'Jinbao Zhang', 'Yatao Zhang']

1705.04824v1

Impervious surface area is a direct consequence of the urbanization, which also plays an important role in urban planning and environmental management. With the rapidly technical development of remote sensing, monitoring urban impervious surface via high spatial resolution (HSR) images has attracted unprecedented attention recently. Traditional multi-classes models are inefficient for impervious surface extraction because it requires labeling all needed and unneeded classes that occur in the image exhaustively. Therefore, we need to find a reliable one-class model to classify one specific land cover type without labeling other classes. In this study, we investigate several one-class classifiers, such as Presence and Background Learning (PBL), Positive Unlabeled Learning (PUL), OCSVM, BSVM and MAXENT, to extract urban impervious surface area using high spatial resolution imagery of GF-1, China's new generation of high spatial remote sensing satellite, and evaluate the classification accuracy based on artificial interpretation results. Compared to traditional multi-classes classifiers (ANN and SVM), the experimental results indicate that PBL and PUL provide higher classification accuracy, which is similar to the accuracy provided by ANN model. Meanwhile, PBL and PUL outperforms OCSVM, BSVM, MAXENT and SVM models. Hence, the one-class classifiers only need a small set of specific samples to train models without losing predictive accuracy, which is supposed to gain more attention on urban impervious surface extraction or other one specific land cover type.

Extracting urban impervious surface from GF-1 imagery using one-class classifiers

http://arxiv.org/pdf/1705.04824v1

Title Extracting urban impervious surface GF1 imagery using oneclass classifier Summary Impervious surface area direct consequence urbanization also play important role urban planning environmental management rapidly technical development remote sensing monitoring urban impervious surface via high spatial resolution HSR image attracted unprecedented attention recently Traditional multiclasses model inefficient impervious surface extraction requires labeling needed unneeded class occur image exhaustively Therefore need find reliable oneclass model classify one specific land cover type without labeling class study investigate several oneclass classifier Presence Background Learning PBL Positive Unlabeled Learning PUL OCSVM BSVM MAXENT extract urban impervious surface area using high spatial resolution imagery GF1 Chinas new generation high spatial remote sensing satellite evaluate classification accuracy based artificial interpretation result Compared traditional multiclasses classifier ANN SVM experimental result indicate PBL PUL provide higher classification accuracy similar accuracy provided ANN model Meanwhile PBL PUL outperforms OCSVM BSVM MAXENT SVM model Hence oneclass classifier need small set specific sample train model without losing predictive accuracy supposed gain attention urban impervious surface extraction one specific land cover type Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Shuchang Zhou', 'Taihong Xiao', 'Yi Yang', 'Dieqiao Feng', 'Qinyao He', 'Weiran He']

1705.04932v1

Object Transfiguration replaces an object in an image with another object from a second image. For example it can perform tasks like "putting exactly those eyeglasses from image A on the nose of the person in image B". Usage of exemplar images allows more precise specification of desired modifications and improves the diversity of conditional image generation. However, previous methods that rely on feature space operations, require paired data and/or appearance models for training or disentangling objects from background. In this work, we propose a model that can learn object transfiguration from two unpaired sets of images: one set containing images that "have" that kind of object, and the other set being the opposite, with the mild constraint that the objects be located approximately at the same place. For example, the training data can be one set of reference face images that have eyeglasses, and another set of images that have not, both of which spatially aligned by face landmarks. Despite the weak 0/1 labels, our model can learn an "eyeglasses" subspace that contain multiple representatives of different types of glasses. Consequently, we can perform fine-grained control of generated images, like swapping the glasses in two images by swapping the projected components in the "eyeglasses" subspace, to create novel images of people wearing eyeglasses. Overall, our deterministic generative model learns disentangled attribute subspaces from weakly labeled data by adversarial training. Experiments on CelebA and Multi-PIE datasets validate the effectiveness of the proposed model on real world data, in generating images with specified eyeglasses, smiling, hair styles, and lighting conditions etc. The code is available online.

GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data

http://arxiv.org/pdf/1705.04932v1

Title GeneGAN Learning Object Transfiguration Attribute Subspace Unpaired Data Summary Object Transfiguration replaces object image another object second image example perform task like putting exactly eyeglass image nose person image B Usage exemplar image allows precise specification desired modification improves diversity conditional image generation However previous method rely feature space operation require paired data andor appearance model training disentangling object background work propose model learn object transfiguration two unpaired set image one set containing image kind object set opposite mild constraint object located approximately place example training data one set reference face image eyeglass another set image spatially aligned face landmark Despite weak 01 label model learn eyeglass subspace contain multiple representative different type glass Consequently perform finegrained control generated image like swapping glass two image swapping projected component eyeglass subspace create novel image people wearing eyeglass Overall deterministic generative model learns disentangled attribute subspace weakly labeled data adversarial training Experiments CelebA MultiPIE datasets validate effectiveness proposed model real world data generating image specified eyeglass smiling hair style lighting condition etc code available online Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yong Khoo']

1705.05016v1

This paper presents a new method for 3D shape reconstruction based on two existing methods. A 3D reconstruction from a single photograph is introduced by both papers: the first one uses a photograph and a set of existing 3D model to generate the 3D object in the photograph, while the second one uses a photograph and a selected similar model to create the 3D object in the photograph. According to their difference, we propose a relaxation based method for more accurate correspondence establishment and shape recovery. The experiment demonstrates promising results compared to the state-of-the-art work on 3D shape estimation.

A Correspondence Relaxation Approach for 3D Shape Reconstruction

http://arxiv.org/pdf/1705.05016v1

Title Correspondence Relaxation Approach 3D Shape Reconstruction Summary paper present new method 3D shape reconstruction based two existing method 3D reconstruction single photograph introduced paper first one us photograph set existing 3D model generate 3D object photograph second one us photograph selected similar model create 3D object photograph According difference propose relaxation based method accurate correspondence establishment shape recovery experiment demonstrates promising result compared stateoftheart work 3D shape estimation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yong Khoo', 'Sang Chung']

1705.05508v1

This paper presents an automated method for 3D character skeleton extraction that can be applied for generic 3D shapes. Our work is motivated by the skeleton-based prior work on automatic rigging focused on skeleton extraction and can automatically aligns the extracted structure to fit the 3D shape of the given 3D mesh. The body mesh can be subsequently skinned based on the extracted skeleton and thus enables rigging process. In the experiment, we apply public dataset to drive the estimated skeleton from different body shapes, as well as the real data obtained from 3D scanning systems. Satisfactory results are obtained compared to the existing approaches.

Automated Body Structure Extraction from Arbitrary 3D Mesh

http://arxiv.org/pdf/1705.05508v1

Title Automated Body Structure Extraction Arbitrary 3D Mesh Summary paper present automated method 3D character skeleton extraction applied generic 3D shape work motivated skeletonbased prior work automatic rigging focused skeleton extraction automatically aligns extracted structure fit 3D shape given 3D mesh body mesh subsequently skinned based extracted skeleton thus enables rigging process experiment apply public dataset drive estimated skeleton different body shape well real data obtained 3D scanning system Satisfactory result obtained compared existing 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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['Ryan Henderson', 'Rasmus Rothe']

1705.05627v3

Picasso is a free open-source (Eclipse Public License) web application written in Python for rendering standard visualizations useful for analyzing convolutional neural networks. Picasso ships with occlusion maps and saliency maps, two visualizations which help reveal issues that evaluation metrics like loss and accuracy might hide: for example, learning a proxy classification task. Picasso works with the Tensorflow deep learning framework, and Keras (when the model can be loaded into the Tensorflow backend). Picasso can be used with minimal configuration by deep learning researchers and engineers alike across various neural network architectures. Adding new visualizations is simple: the user can specify their visualization code and HTML template separately from the application code.

Picasso: A Modular Framework for Visualizing the Learning Process of Neural Network Image Classifiers

http://arxiv.org/pdf/1705.05627v3

Title Picasso Modular Framework Visualizing Learning Process Neural Network Image Classifiers Summary Picasso free opensource Eclipse Public License web application written Python rendering standard visualization useful analyzing convolutional neural network Picasso ship occlusion map saliency map two visualization help reveal issue evaluation metric like loss accuracy might hide example learning proxy classification task Picasso work Tensorflow deep learning framework Keras model loaded Tensorflow backend Picasso used minimal configuration deep learning researcher engineer alike across various neural network architecture Adding new visualization simple user specify visualization code HTML template separately application code Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Youngjun Cho', 'Simon J. Julier', 'Nicolai Marquardt', 'Nadia Bianchi-Berthouze']

1705.06628v2

The ability to monitor respiratory rate is extremely important for medical treatment, healthcare and fitness sectors. In many situations, mobile methods, which allow users to undertake every day activities, are required. However, current monitoring systems can be obtrusive, requiring users to wear respiration belts or nasal probes. Recent advances in thermographic systems have shrunk their size, weight and cost, to the point where it is possible to create smart-phone based respiration rate monitoring devices that are not affected by lighting conditions. However, mobile thermal imaging is challenged in scenes with high thermal dynamic ranges. This challenge is further amplified by general problems such as motion artifacts and low spatial resolution, leading to unreliable breathing signals. In this paper, we propose a novel and robust approach for respiration tracking which compensates for the negative effects of variations in the ambient temperature and motion artifacts and can accurately extract breathing rates in highly dynamic thermal scenes. It has three main contributions. The first is a novel Optimal Quantization technique which adaptively constructs a color mapping of absolute temperature to improve segmentation, classification and tracking. The second is the Thermal Gradient Flow method that computes thermal gradient magnitude maps to enhance accuracy of the nostril region tracking. Finally, we introduce the Thermal Voxel method to increase the reliability of the captured respiration signals compared to the traditional averaging method. We demonstrate the extreme robustness of our system to track the nostril-region and measure the respiratory rate in high dynamic range scenes.

Robust tracking of respiratory rate in high-dynamic range scenes using mobile thermal imaging

http://arxiv.org/pdf/1705.06628v2

Title Robust tracking respiratory rate highdynamic range scene using mobile thermal imaging Summary ability monitor respiratory rate extremely important medical treatment healthcare fitness sector many situation mobile method allow user undertake every day activity required However current monitoring system obtrusive requiring user wear respiration belt nasal probe Recent advance thermographic system shrunk size weight cost point possible create smartphone based respiration rate monitoring device affected lighting condition However mobile thermal imaging challenged scene high thermal dynamic range challenge amplified general problem motion artifact low spatial resolution leading unreliable breathing signal paper propose novel robust approach respiration tracking compensates negative effect variation ambient temperature motion artifact accurately extract breathing rate highly dynamic thermal scene three main contribution first novel Optimal Quantization technique adaptively construct color mapping absolute temperature improve segmentation classification tracking second Thermal Gradient Flow method computes thermal gradient magnitude map enhance accuracy nostril region tracking Finally introduce Thermal Voxel method increase reliability captured respiration signal compared traditional averaging method demonstrate extreme robustness system track nostrilregion measure respiratory rate high dynamic range scene Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Clara Callenberg', 'Felix Heide', 'Gordon Wetzstein', 'Matthias Hullin']

1705.07108v1

Computational photography encompasses a diversity of imaging techniques, but one of the core operations performed by many of them is to compute image differences. An intuitive approach to computing such differences is to capture several images sequentially and then process them jointly. Usually, this approach leads to artifacts when recording dynamic scenes. In this paper, we introduce a snapshot difference imaging approach that is directly implemented in the sensor hardware of emerging time-of-flight cameras. With a variety of examples, we demonstrate that the proposed snapshot difference imaging technique is useful for direct-global illumination separation, for direct imaging of spatial and temporal image gradients, for direct depth edge imaging, and more.

Snapshot Difference Imaging using Time-of-Flight Sensors

http://arxiv.org/pdf/1705.07108v1

Title Snapshot Difference Imaging using TimeofFlight Sensors Summary Computational photography encompasses diversity imaging technique one core operation performed many compute image difference intuitive approach computing difference capture several image sequentially process jointly Usually approach lead artifact recording dynamic scene paper introduce snapshot difference imaging approach directly implemented sensor hardware emerging timeofflight camera variety example demonstrate proposed snapshot difference imaging technique useful directglobal illumination separation direct imaging spatial temporal image gradient direct depth edge imaging Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hye-Rin Kim', 'Yeong-Seok Kim', 'Seon Joo Kim', 'In-Kwon Lee']

1705.07543v2

An image is a very effective tool for conveying emotions. Many researchers have investigated in computing the image emotions by using various features extracted from images. In this paper, we focus on two high level features, the object and the background, and assume that the semantic information of images is a good cue for predicting emotion. An object is one of the most important elements that define an image, and we find out through experiments that there is a high correlation between the object and the emotion in images. Even with the same object, there may be slight difference in emotion due to different backgrounds, and we use the semantic information of the background to improve the prediction performance. By combining the different levels of features, we build an emotion based feed forward deep neural network which produces the emotion values of a given image. The output emotion values in our framework are continuous values in the 2-dimensional space (Valence and Arousal), which are more effective than using a few number of emotion categories in describing emotions. Experiments confirm the effectiveness of our network in predicting the emotion of images.

Building Emotional Machines: Recognizing Image Emotions through Deep Neural Networks

http://arxiv.org/pdf/1705.07543v2

Title Building Emotional Machines Recognizing Image Emotions Deep Neural Networks Summary image effective tool conveying emotion Many researcher investigated computing image emotion using various feature extracted image paper focus two high level feature object background assume semantic information image good cue predicting emotion object one important element define image find experiment high correlation object emotion image Even object may slight difference emotion due different background use semantic information background improve prediction performance combining different level feature build emotion based feed forward deep neural network produce emotion value given image output emotion value framework continuous value 2dimensional space Valence Arousal effective using number emotion category describing emotion Experiments confirm effectiveness network predicting emotion 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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['Stan Melax', 'Leonid Keselman', 'Sterling Orsten']

1705.07640v1

Tracking the full skeletal pose of the hands and fingers is a challenging problem that has a plethora of applications for user interaction. Existing techniques either require wearable hardware, add restrictions to user pose, or require significant computation resources. This research explores a new approach to tracking hands, or any articulated model, by using an augmented rigid body simulation. This allows us to phrase 3D object tracking as a linear complementarity problem with a well-defined solution. Based on a depth sensor's samples, the system generates constraints that limit motion orthogonal to the rigid body model's surface. These constraints, along with prior motion, collision/contact constraints, and joint mechanics, are resolved with a projected Gauss-Seidel solver. Due to camera noise properties and attachment errors, the numerous surface constraints are impulse capped to avoid overpowering mechanical constraints. To improve tracking accuracy, multiple simulations are spawned at each frame and fed a variety of heuristics, constraints and poses. A 3D error metric selects the best-fit simulation, helping the system handle challenging hand motions. Such an approach enables real-time, robust, and accurate 3D skeletal tracking of a user's hand on a variety of depth cameras, while only utilizing a single x86 CPU core for processing.

Dynamics Based 3D Skeletal Hand Tracking

http://arxiv.org/pdf/1705.07640v1

Title Dynamics Based 3D Skeletal Hand Tracking Summary Tracking full skeletal pose hand finger challenging problem plethora application user interaction Existing technique either require wearable hardware add restriction user pose require significant computation resource research explores new approach tracking hand articulated model using augmented rigid body simulation allows u phrase 3D object tracking linear complementarity problem welldefined solution Based depth sensor sample system generates constraint limit motion orthogonal rigid body model surface constraint along prior motion collisioncontact constraint joint mechanic resolved projected GaussSeidel solver Due camera noise property attachment error numerous surface constraint impulse capped avoid overpowering mechanical constraint improve tracking accuracy multiple simulation spawned frame fed variety heuristic constraint pose 3D error metric selects bestfit simulation helping system handle challenging hand motion approach enables realtime robust accurate 3D skeletal tracking user hand variety depth camera utilizing single x86 CPU core processing Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Michael Gygli']

1705.08214v1

Shot boundary detection (SBD) is an important component of many video analysis tasks, such as action recognition, video indexing, summarization and editing. Previous work typically used a combination of low-level features like color histograms, in conjunction with simple models such as SVMs. Instead, we propose to learn shot detection end-to-end, from pixels to final shot boundaries. For training such a model, we rely on our insight that all shot boundaries are generated. Thus, we create a dataset with one million frames and automatically generated transitions such as cuts, dissolves and fades. In order to efficiently analyze hours of videos, we propose a Convolutional Neural Network (CNN) which is fully convolutional in time, thus allowing to use a large temporal context without the need to repeatedly processing frames. With this architecture our method obtains state-of-the-art results while running at an unprecedented speed of more than 120x real-time.

Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks

http://arxiv.org/pdf/1705.08214v1

Title Ridiculously Fast Shot Boundary Detection Fully Convolutional Neural Networks Summary Shot boundary detection SBD important component many video analysis task action recognition video indexing summarization editing Previous work typically used combination lowlevel feature like color histogram conjunction simple model SVMs Instead propose learn shot detection endtoend pixel final shot boundary training model rely insight shot boundary generated Thus create dataset one million frame automatically generated transition cut dissolve fade order efficiently analyze hour video propose Convolutional Neural Network CNN fully convolutional time thus allowing use large temporal context without need repeatedly processing frame architecture method obtains stateoftheart result running unprecedented speed 120x realtime Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Menglong Ye', 'Edward Johns', 'Ankur Handa', 'Lin Zhang', 'Philip Pratt', 'Guang-Zhong Yang']

1705.08260v1

Robotic surgery has become a powerful tool for performing minimally invasive procedures, providing advantages in dexterity, precision, and 3D vision, over traditional surgery. One popular robotic system is the da Vinci surgical platform, which allows preoperative information to be incorporated into live procedures using Augmented Reality (AR). Scene depth estimation is a prerequisite for AR, as accurate registration requires 3D correspondences between preoperative and intraoperative organ models. In the past decade, there has been much progress on depth estimation for surgical scenes, such as using monocular or binocular laparoscopes [1,2]. More recently, advances in deep learning have enabled depth estimation via Convolutional Neural Networks (CNNs) [3], but training requires a large image dataset with ground truth depths. Inspired by [4], we propose a deep learning framework for surgical scene depth estimation using self-supervision for scalable data acquisition. Our framework consists of an autoencoder for depth prediction, and a differentiable spatial transformer for training the autoencoder on stereo image pairs without ground truth depths. Validation was conducted on stereo videos collected in robotic partial nephrectomy.

Self-Supervised Siamese Learning on Stereo Image Pairs for Depth Estimation in Robotic Surgery

http://arxiv.org/pdf/1705.08260v1

Title SelfSupervised Siamese Learning Stereo Image Pairs Depth Estimation Robotic Surgery Summary Robotic surgery become powerful tool performing minimally invasive procedure providing advantage dexterity precision 3D vision traditional surgery One popular robotic system da Vinci surgical platform allows preoperative information incorporated live procedure using Augmented Reality AR Scene depth estimation prerequisite AR accurate registration requires 3D correspondence preoperative intraoperative organ model past decade much progress depth estimation surgical scene using monocular binocular laparoscope 12 recently advance deep learning enabled depth estimation via Convolutional Neural Networks CNNs 3 training requires large image dataset ground truth depth Inspired 4 propose deep learning framework surgical scene depth estimation using selfsupervision scalable data acquisition framework consists autoencoder depth prediction differentiable spatial transformer training autoencoder stereo image pair without ground truth depth Validation conducted stereo video collected robotic partial nephrectomy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['David Barina', 'Michal Kula', 'Michal Matysek', 'Pavel Zemcik']

1705.08266v1

The two-dimensional discrete wavelet transform has a huge number of applications in image-processing techniques. Until now, several papers compared the performance of such transform on graphics processing units (GPUs). However, all of them only dealt with lifting and convolution computation schemes. In this paper, we show that corresponding horizontal and vertical lifting parts of the lifting scheme can be merged into non-separable lifting units, which halves the number of steps. We also discuss an optimization strategy leading to a reduction in the number of arithmetic operations. The schemes were assessed using the OpenCL and pixel shaders. The proposed non-separable lifting scheme outperforms the existing schemes in many cases, irrespective of its higher complexity.

Accelerating Discrete Wavelet Transforms on GPUs

http://arxiv.org/pdf/1705.08266v1

Title Accelerating Discrete Wavelet Transforms GPUs Summary twodimensional discrete wavelet transform huge number application imageprocessing technique several paper compared performance transform graphic processing unit GPUs However dealt lifting convolution computation scheme paper show corresponding horizontal vertical lifting part lifting scheme merged nonseparable lifting unit half number step also discus optimization strategy leading reduction number arithmetic operation scheme assessed using OpenCL pixel shaders proposed nonseparable lifting scheme outperforms existing scheme many case irrespective higher complexity Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Quentin Bateux', 'Eric Marchand', 'Jürgen Leitner', 'Francois Chaumette', 'Peter Corke']

1705.08940v2

We present a deep neural network-based method to perform high-precision, robust and real-time 6 DOF visual servoing. The paper describes how to create a dataset simulating various perturbations (occlusions and lighting conditions) from a single real-world image of the scene. A convolutional neural network is fine-tuned using this dataset to estimate the relative pose between two images of the same scene. The output of the network is then employed in a visual servoing control scheme. The method converges robustly even in difficult real-world settings with strong lighting variations and occlusions.A positioning error of less than one millimeter is obtained in experiments with a 6 DOF robot.

Visual Servoing from Deep Neural Networks

http://arxiv.org/pdf/1705.08940v2

Title Visual Servoing Deep Neural Networks Summary present deep neural networkbased method perform highprecision robust realtime 6 DOF visual servoing paper describes create dataset simulating various perturbation occlusion lighting condition single realworld image scene convolutional neural network finetuned using dataset estimate relative pose two image scene output network employed visual servoing control scheme method converges robustly even difficult realworld setting strong lighting variation occlusionsA positioning error le one millimeter obtained experiment 6 DOF robot Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Quanzeng You', 'Darío García-García', 'Mahohar Paluri', 'Jiebo Luo', 'Jungseock Joo']

1705.08974v1

Online social media is a social vehicle in which people share various moments of their lives with their friends, such as playing sports, cooking dinner or just taking a selfie for fun, via visual means, that is, photographs. Our study takes a closer look at the popular visual concepts illustrating various cultural lifestyles from aggregated, de-identified photographs. We perform analysis both at macroscopic and microscopic levels, to gain novel insights about global and local visual trends as well as the dynamics of interpersonal cultural exchange and diffusion among Facebook friends. We processed images by automatically classifying the visual content by a convolutional neural network (CNN). Through various statistical tests, we find that socially tied individuals more likely post images showing similar cultural lifestyles. To further identify the main cause of the observed social correlation, we use the Shuffle test and the Preference-based Matched Estimation (PME) test to distinguish the effects of influence and homophily. The results indicate that the visual content of each user's photographs are temporally, although not necessarily causally, correlated with the photographs of their friends, which may suggest the effect of influence. Our paper demonstrates that Facebook photographs exhibit diverse cultural lifestyles and preferences and that the social interaction mediated through the visual channel in social media can be an effective mechanism for cultural diffusion.

Cultural Diffusion and Trends in Facebook Photographs

http://arxiv.org/pdf/1705.08974v1

Title Cultural Diffusion Trends Facebook Photographs Summary Online social medium social vehicle people share various moment life friend playing sport cooking dinner taking selfie fun via visual mean photograph study take closer look popular visual concept illustrating various cultural lifestyle aggregated deidentified photograph perform analysis macroscopic microscopic level gain novel insight global local visual trend well dynamic interpersonal cultural exchange diffusion among Facebook friend processed image automatically classifying visual content convolutional neural network CNN various statistical test find socially tied individual likely post image showing similar cultural lifestyle identify main cause observed social correlation use Shuffle test Preferencebased Matched Estimation PME test distinguish effect influence homophily result indicate visual content user photograph temporally although necessarily causally correlated photograph friend may suggest effect influence paper demonstrates Facebook photograph exhibit diverse cultural lifestyle preference social interaction mediated visual channel social medium effective mechanism cultural diffusion Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Gregery T. Buzzard', 'Stanley H. Chan', 'Charles A. Bouman']

1705.08983v2

Regularized inversion methods for image reconstruction are used widely due to their tractability and their ability to combine physical sensor models with useful regularity criteria. Such methods were used in the recently developed Plug-and-Play prior method, which provides a framework to use advanced denoisers as regularizers in inversion. However, the need to formulate regularized inversion as the solution to an optimization problem limits the expressiveness of possible regularity conditions and the variety of provably convergent Plug-and-Play denoising operators. In this paper, we introduce the idea of Consensus Equilibrium (CE), which generalizes regularized inversion to include a wide variety of regularity operators without the need for an optimization formulation. CE is based on the solution of a set of equilibrium equations that balance data fit and regularity. In CE, the problem of MAP estimation in regularized inversion is replaced by the problem of solving these equations, which can be approached in multiple ways. The key contribution of CE is to provide a novel framework for fusing a wide variety of regularizing operators with physical sensor models, even for models and operators that are not expressible via optimization. We describe the derivation of the CE equations and prove that the solution of the CE equations generalizes the standard MAP estimate. We also discuss algorithms for solving the CE equations, including a version of the Douglas-Rachford (DR)/ADMM algorithm with a novel form of preconditioning and Newton's method, both standard form and a Jacobian-free form. We illustrate the idea of consensus equilibrium on several examples, one using an array of convolutional neural network denoisers, none of which is tuned to match the noise level in a noisy image but which in consensus can achieve a better result than any of them individually.

Plug-and-Play Unplugged: Optimization Free Reconstruction using Consensus Equilibrium

http://arxiv.org/pdf/1705.08983v2

Title PlugandPlay Unplugged Optimization Free Reconstruction using Consensus Equilibrium Summary Regularized inversion method image reconstruction used widely due tractability ability combine physical sensor model useful regularity criterion method used recently developed PlugandPlay prior method provides framework use advanced denoisers regularizers inversion However need formulate regularized inversion solution optimization problem limit expressiveness possible regularity condition variety provably convergent PlugandPlay denoising operator paper introduce idea Consensus Equilibrium CE generalizes regularized inversion include wide variety regularity operator without need optimization formulation CE based solution set equilibrium equation balance data fit regularity CE problem MAP estimation regularized inversion replaced problem solving equation approached multiple way key contribution CE provide novel framework fusing wide variety regularizing operator physical sensor model even model operator expressible via optimization describe derivation CE equation prove solution CE equation generalizes standard MAP estimate also discus algorithm solving CE equation including version DouglasRachford DRADMM algorithm novel form preconditioning Newtons method standard form Jacobianfree form illustrate idea consensus equilibrium several example one using array convolutional neural network denoisers none tuned match noise level noisy image consensus achieve better result individually Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Milad Mozafari', 'Saeed Reza Kheradpisheh', 'Timothée Masquelier', 'Abbas Nowzari-Dalini', 'Mohammad Ganjtabesh']

1705.09132v2

Reinforcement learning (RL) has recently regained popularity, with major achievements such as beating the European game of Go champion. Here, for the first time, we show that RL can be used efficiently to train a spiking neural network (SNN) to perform object recognition in natural images without using an external classifier. We used a feedforward convolutional SNN and a temporal coding scheme where the most strongly activated neurons fire first, while less activated ones fire later, or not at all. In the highest layers, each neuron was assigned to an object category, and it was assumed that the stimulus category was the category of the first neuron to fire. If this assumption was correct, the neuron was rewarded, i.e. spike-timing-dependent plasticity (STDP) was applied, which reinforced the neuron's selectivity. Otherwise, anti-STDP was applied, which encouraged the neuron to learn something else. As demonstrated on various image datasets (Caltech, ETH-80, and NORB), this reward modulated STDP (R-STDP) approach extracted particularly discriminative visual features, whereas classic unsupervised STDP extracts any feature that consistently repeats. As a result, R-STDP outperformed STDP on these datasets. Furthermore, R-STDP is suitable for online learning, and can adapt to drastic changes such as label permutations. Finally, it is worth mentioning that both feature extraction and classification were done with spikes, using at most one spike per neuron. Thus the network is hardware friendly and energy efficient.

First-spike based visual categorization using reward-modulated STDP

http://arxiv.org/pdf/1705.09132v2

Title Firstspike based visual categorization using rewardmodulated STDP Summary Reinforcement learning RL recently regained popularity major achievement beating European game Go champion first time show RL used efficiently train spiking neural network SNN perform object recognition natural image without using external classifier used feedforward convolutional SNN temporal coding scheme strongly activated neuron fire first le activated one fire later highest layer neuron assigned object category assumed stimulus category category first neuron fire assumption correct neuron rewarded ie spiketimingdependent plasticity STDP applied reinforced neuron selectivity Otherwise antiSTDP applied encouraged neuron learn something else demonstrated various image datasets Caltech ETH80 NORB reward modulated STDP RSTDP approach extracted particularly discriminative visual feature whereas classic unsupervised STDP extract feature consistently repeat result RSTDP outperformed STDP datasets Furthermore RSTDP suitable online learning adapt drastic change label permutation Finally worth mentioning feature extraction classification done spike using one spike per neuron Thus network hardware friendly energy efficient Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Wenjian Hu', 'Krishna Kumar Singh', 'Fanyi Xiao', 'Jinyoung Han', 'Chen-Nee Chuah', 'Yong Jae Lee']

1705.09275v4

Content popularity prediction has been extensively studied due to its importance and interest for both users and hosts of social media sites like Facebook, Instagram, Twitter, and Pinterest. However, existing work mainly focuses on modeling popularity using a single metric such as the total number of likes or shares. In this work, we propose Diffusion-LSTM, a memory-based deep recurrent network that learns to recursively predict the entire diffusion path of an image through a social network. By combining user social features and image features, and encoding the diffusion path taken thus far with an explicit memory cell, our model predicts the diffusion path of an image more accurately compared to alternate baselines that either encode only image or social features, or lack memory. By mapping individual users to user prototypes, our model can generalize to new users not seen during training. Finally, we demonstrate our model's capability of generating diffusion trees, and show that the generated trees closely resemble ground-truth trees.

Who Will Share My Image? Predicting the Content Diffusion Path in Online Social Networks

http://arxiv.org/pdf/1705.09275v4

Title Share Image Predicting Content Diffusion Path Online Social Networks Summary Content popularity prediction extensively studied due importance interest user host social medium site like Facebook Instagram Twitter Pinterest However existing work mainly focus modeling popularity using single metric total number like share work propose DiffusionLSTM memorybased deep recurrent network learns recursively predict entire diffusion path image social network combining user social feature image feature encoding diffusion path taken thus far explicit memory cell model predicts diffusion path image accurately compared alternate baseline either encode image social feature lack memory mapping individual user user prototype model generalize new user seen training Finally demonstrate model capability generating diffusion tree show generated tree closely resemble groundtruth tree Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yue Wu', 'Wael AbdAlmageed', 'Prem Natarajan']

1705.09765v1

Image splicing is a very common image manipulation technique that is sometimes used for malicious purposes. A splicing detec- tion and localization algorithm usually takes an input image and produces a binary decision indicating whether the input image has been manipulated, and also a segmentation mask that corre- sponds to the spliced region. Most existing splicing detection and localization pipelines suffer from two main shortcomings: 1) they use handcrafted features that are not robust against subsequent processing (e.g., compression), and 2) each stage of the pipeline is usually optimized independently. In this paper we extend the formulation of the underlying splicing problem to consider two input images, a query image and a potential donor image. Here the task is to estimate the probability that the donor image has been used to splice the query image, and obtain the splicing masks for both the query and donor images. We introduce a novel deep convolutional neural network architecture, called Deep Matching and Validation Network (DMVN), which simultaneously localizes and detects image splicing. The proposed approach does not depend on handcrafted features and uses raw input images to create deep learned representations. Furthermore, the DMVN is end-to-end op- timized to produce the probability estimates and the segmentation masks. Our extensive experiments demonstrate that this approach outperforms state-of-the-art splicing detection methods by a large margin in terms of both AUC score and speed.

Deep Matching and Validation Network -- An End-to-End Solution to Constrained Image Splicing Localization and Detection

http://arxiv.org/pdf/1705.09765v1

Title Deep Matching Validation Network EndtoEnd Solution Constrained Image Splicing Localization Detection Summary Image splicing common image manipulation technique sometimes used malicious purpose splicing detec tion localization algorithm usually take input image produce binary decision indicating whether input image manipulated also segmentation mask corre sponds spliced region existing splicing detection localization pipeline suffer two main shortcoming 1 use handcrafted feature robust subsequent processing eg compression 2 stage pipeline usually optimized independently paper extend formulation underlying splicing problem consider two input image query image potential donor image task estimate probability donor image used splice query image obtain splicing mask query donor image introduce novel deep convolutional neural network architecture called Deep Matching Validation Network DMVN simultaneously localizes detects image splicing proposed approach depend handcrafted feature us raw input image create deep learned representation Furthermore DMVN endtoend op timized produce probability estimate segmentation mask extensive experiment demonstrate approach outperforms stateoftheart splicing detection method large margin term AUC score speed Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ankit Dhall', 'Kunal Chelani', 'Vishnu Radhakrishnan', 'K. M. Krishna']

1705.09785v1

With the advent of autonomous vehicles, LiDAR and cameras have become an indispensable combination of sensors. They both provide rich and complementary data which can be used by various algorithms and machine learning to sense and make vital inferences about the surroundings. We propose a novel pipeline and experimental setup to find accurate rigid-body transformation for extrinsically calibrating a LiDAR and a camera. The pipeling uses 3D-3D point correspondences in LiDAR and camera frame and gives a closed form solution. We further show the accuracy of the estimate by fusing point clouds from two stereo cameras which align perfectly with the rotation and translation estimated by our method, confirming the accuracy of our method's estimates both mathematically and visually. Taking our idea of extrinsic LiDAR-camera calibration forward, we demonstrate how two cameras with no overlapping field-of-view can also be calibrated extrinsically using 3D point correspondences. The code has been made available as open-source software in the form of a ROS package, more information about which can be sought here: https://github.com/ankitdhall/lidar_camera_calibration .

LiDAR-Camera Calibration using 3D-3D Point correspondences

http://arxiv.org/pdf/1705.09785v1

Title LiDARCamera Calibration using 3D3D Point correspondence Summary advent autonomous vehicle LiDAR camera become indispensable combination sensor provide rich complementary data used various algorithm machine learning sense make vital inference surroundings propose novel pipeline experimental setup find accurate rigidbody transformation extrinsically calibrating LiDAR camera pipeling us 3D3D point correspondence LiDAR camera frame give closed form solution show accuracy estimate fusing point cloud two stereo camera align perfectly rotation translation estimated method confirming accuracy method estimate mathematically visually Taking idea extrinsic LiDARcamera calibration forward demonstrate two camera overlapping fieldofview also calibrated extrinsically using 3D point correspondence code made available opensource software form ROS package information sought httpsgithubcomankitdhalllidarcameracalibration Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Arturo Deza', 'Aditya Jonnalagadda', 'Miguel Eckstein']

1705.10041v2

Given the recent successes of deep learning applied to style transfer and texture synthesis, we propose a new theoretical framework to construct visual metamers: \textit{a family of perceptually identical, yet physically different images}. We review work both in neuroscience related to metameric stimuli, as well as computer vision research in style transfer. We propose our NeuroFovea metamer model that is based on a mixture of peripheral representations and style transfer forward-pass algorithms for \emph{any} image from the recent work of Adaptive Instance Normalization (Huang~\&~Belongie). Our model is parametrized by a VGG-Net versus a set of joint statistics of complex wavelet coefficients which allows us to encode images in high dimensional space and interpolate between the content and texture information. We empirically show that human observers discriminate our metamers at a similar rate as the metamers of Freeman~\&~Simoncelli (FS) In addition, our NeuroFovea metamer model gives us the benefit of near real-time generation which presents a $\times1000$ speed-up compared to previous work. Critically, psychophysical studies show that both the FS and NeuroFovea metamers are discriminable from the original images highlighting an important limitation of current metamer generation methods.

Towards Metamerism via Foveated Style Transfer

http://arxiv.org/pdf/1705.10041v2

Title Towards Metamerism via Foveated Style Transfer Summary Given recent success deep learning applied style transfer texture synthesis propose new theoretical framework construct visual metamers textita family perceptually identical yet physically different image review work neuroscience related metameric stimulus well computer vision research style transfer propose NeuroFovea metamer model based mixture peripheral representation style transfer forwardpass algorithm emphany image recent work Adaptive Instance Normalization HuangBelongie model parametrized VGGNet versus set joint statistic complex wavelet coefficient allows u encode image high dimensional space interpolate content texture information empirically show human observer discriminate metamers similar rate metamers FreemanSimoncelli FS addition NeuroFovea metamer model give u benefit near realtime generation present times1000 speedup compared previous work Critically psychophysical study show FS NeuroFovea metamers discriminable original image highlighting important limitation current metamer generation 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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['Francisco J. Simois', 'Juan J. Murillo-Fuentes']

1705.10060v1

A routine task for art historians is painting diagnostics, such as dating or attribution. Signal processing of the X-ray image of a canvas provides useful information about its fabric. However, previous methods may fail when very old and deteriorated artworks or simply canvases of small size are studied. We present a new framework to analyze and further characterize the paintings from their radiographs. First, we start from a general analysis of lattices and provide new unifying results about the theoretical spectra of weaves. Then, we use these results to infer the main structure of the fabric, like the type of weave and the thread densities. We propose a practical estimation of these theoretical results from paintings with the averaged power spectral density (PSD), which provides a more robust tool. Furthermore, we found that the PSD provides a fingerprint that characterizes the whole canvas. We search and discuss some distinctive features we may find in that fingerprint. We apply these results to several masterpieces of the 17th and 18th centuries from the Museo Nacional del Prado to show that this approach yields accurate results in thread counting and is very useful for paintings comparison, even in situations where previous methods fail.

On the Power Spectral Density Applied to the Analysis of Old Canvases

http://arxiv.org/pdf/1705.10060v1

Title Power Spectral Density Applied Analysis Old Canvases Summary routine task art historian painting diagnostics dating attribution Signal processing Xray image canvas provides useful information fabric However previous method may fail old deteriorated artwork simply canvas small size studied present new framework analyze characterize painting radiograph First start general analysis lattice provide new unifying result theoretical spectrum weave use result infer main structure fabric like type weave thread density propose practical estimation theoretical result painting averaged power spectral density PSD provides robust tool Furthermore found PSD provides fingerprint characterizes whole canvas search discus distinctive feature may find fingerprint apply result several masterpiece 17th 18th century Museo Nacional del Prado show approach yield accurate result thread counting useful painting comparison even situation previous method fail Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Lorien X. Hayden', 'Alexander A. Alemi', 'Paul H. Ginsparg', 'James P. Sethna']

1705.10589v1

Neural networks have been shown to have a remarkable ability to uncover low dimensional structure in data: the space of possible reconstructed images form a reduced model manifold in image space. We explore this idea directly by analyzing the manifold learned by Deep Belief Networks and Stacked Denoising Autoencoders using Monte Carlo sampling. The model manifold forms an only slightly elongated hyperball with actual reconstructed data appearing predominantly on the boundaries of the manifold. In connection with the results we present, we discuss problems of sampling high-dimensional manifolds as well as recent work [M. Transtrum, G. Hart, and P. Qiu, Submitted (2014)] discussing the relation between high dimensional geometry and model reduction.

Jeffrey's prior sampling of deep sigmoidal networks

http://arxiv.org/pdf/1705.10589v1

Title Jeffreys prior sampling deep sigmoidal network Summary Neural network shown remarkable ability uncover low dimensional structure data space possible reconstructed image form reduced model manifold image space explore idea directly analyzing manifold learned Deep Belief Networks Stacked Denoising Autoencoders using Monte Carlo sampling model manifold form slightly elongated hyperball actual reconstructed data appearing predominantly boundary manifold connection result present discus problem sampling highdimensional manifold well recent work Transtrum G Hart P Qiu Submitted 2014 discussing relation high dimensional geometry model reduction Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Titus Cieslewski', 'Davide Scaramuzza']

1705.10739v1

In this paper, we discuss the adaptation of our decentralized place recognition method described in [1] to full image descriptors. As we had shown, the key to making a scalable decentralized visual place recognition lies in exploting deterministic key assignment in a distributed key-value map. Through this, it is possible to reduce bandwidth by up to a factor of n, the robot count, by casting visual place recognition to a key-value lookup problem. In [1], we exploited this for the bag-of-words method [3], [4]. Our method of casting bag-of-words, however, results in a complex decentralized system, which has inherently worse recall than its centralized counterpart. In this paper, we instead start from the recent full-image description method NetVLAD [5]. As we show, casting this to a key-value lookup problem can be achieved with k-means clustering, and results in a much simpler system than [1]. The resulting system still has some flaws, albeit of a completely different nature: it suffers when the environment seen during deployment lies in a different distribution in feature space than the environment seen during training.

Efficient Decentralized Visual Place Recognition From Full-Image Descriptors

http://arxiv.org/pdf/1705.10739v1

Title Efficient Decentralized Visual Place Recognition FullImage Descriptors Summary paper discus adaptation decentralized place recognition method described 1 full image descriptor shown key making scalable decentralized visual place recognition lie exploting deterministic key assignment distributed keyvalue map possible reduce bandwidth factor n robot count casting visual place recognition keyvalue lookup problem 1 exploited bagofwords method 3 4 method casting bagofwords however result complex decentralized system inherently worse recall centralized counterpart paper instead start recent fullimage description method NetVLAD 5 show casting keyvalue lookup problem achieved kmeans clustering result much simpler system 1 resulting system still flaw albeit completely different nature suffers environment seen deployment lie different distribution feature space environment seen training Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Fuwen Tan', 'Crispin Bernier', 'Benjamin Cohen', 'Vicente Ordonez', 'Connelly Barnes']

1706.01021v2

Image compositing is a method used to generate realistic yet fake imagery by inserting contents from one image to another. Previous work in compositing has focused on improving appearance compatibility of a user selected foreground segment and a background image (i.e. color and illumination consistency). In this work, we instead develop a fully automated compositing model that additionally learns to select and transform compatible foreground segments from a large collection given only an input image background. To simplify the task, we restrict our problem by focusing on human instance composition, because human segments exhibit strong correlations with their background and because of the availability of large annotated data. We develop a novel branching Convolutional Neural Network (CNN) that jointly predicts candidate person locations given a background image. We then use pre-trained deep feature representations to retrieve person instances from a large segment database. Experimental results show that our model can generate composite images that look visually convincing. We also develop a user interface to demonstrate the potential application of our method.

Where and Who? Automatic Semantic-Aware Person Composition

http://arxiv.org/pdf/1706.01021v2

Title Automatic SemanticAware Person Composition Summary Image compositing method used generate realistic yet fake imagery inserting content one image another Previous work compositing focused improving appearance compatibility user selected foreground segment background image ie color illumination consistency work instead develop fully automated compositing model additionally learns select transform compatible foreground segment large collection given input image background simplify task restrict problem focusing human instance composition human segment exhibit strong correlation background availability large annotated data develop novel branching Convolutional Neural Network CNN jointly predicts candidate person location given background image use pretrained deep feature representation retrieve person instance large segment database Experimental result show model generate composite image look visually convincing also develop user interface demonstrate potential application 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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['Jian Cheng', 'Peter J. Basser']

1706.01862v2

In Diffusion Tensor Imaging (DTI) or High Angular Resolution Diffusion Imaging (HARDI), a tensor field or a spherical function field (e.g., an orientation distribution function field), can be estimated from measured diffusion weighted images. In this paper, inspired by the microscopic theoretical treatment of phases in liquid crystals, we introduce a novel mathematical framework, called Director Field Analysis (DFA), to study local geometric structural information of white matter based on the reconstructed tensor field or spherical function field: 1) We propose a set of mathematical tools to process general director data, which consists of dyadic tensors that have orientations but no direction. 2) We propose Orientational Order (OO) and Orientational Dispersion (OD) indices to describe the degree of alignment and dispersion of a spherical function in a single voxel or in a region, respectively; 3) We also show how to construct a local orthogonal coordinate frame in each voxel exhibiting anisotropic diffusion; 4) Finally, we define three indices to describe three types of orientational distortion (splay, bend, and twist) in a local spatial neighborhood, and a total distortion index to describe distortions of all three types. To our knowledge, this is the first work to quantitatively describe orientational distortion (splay, bend, and twist) in general spherical function fields from DTI or HARDI data. The proposed DFA and its related mathematical tools can be used to process not only diffusion MRI data but also general director field data, and the proposed scalar indices are useful for detecting local geometric changes of white matter for voxel-based or tract-based analysis in both DTI and HARDI acquisitions. The related codes and a tutorial for DFA will be released in DMRITool.

Director Field Analysis (DFA): Exploring Local White Matter Geometric Structure in diffusion MRI

http://arxiv.org/pdf/1706.01862v2

Title Director Field Analysis DFA Exploring Local White Matter Geometric Structure diffusion MRI Summary Diffusion Tensor Imaging DTI High Angular Resolution Diffusion Imaging HARDI tensor field spherical function field eg orientation distribution function field estimated measured diffusion weighted image paper inspired microscopic theoretical treatment phase liquid crystal introduce novel mathematical framework called Director Field Analysis DFA study local geometric structural information white matter based reconstructed tensor field spherical function field 1 propose set mathematical tool process general director data consists dyadic tensor orientation direction 2 propose Orientational Order OO Orientational Dispersion OD index describe degree alignment dispersion spherical function single voxel region respectively 3 also show construct local orthogonal coordinate frame voxel exhibiting anisotropic diffusion 4 Finally define three index describe three type orientational distortion splay bend twist local spatial neighborhood total distortion index describe distortion three type knowledge first work quantitatively describe orientational distortion splay bend twist general spherical function field DTI HARDI data proposed DFA related mathematical tool used process diffusion MRI data also general director field data proposed scalar index useful detecting local geometric change white matter voxelbased tractbased analysis DTI HARDI acquisition related code tutorial DFA released DMRITool Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Xiaoguang Han', 'Chang Gao', 'Yizhou Yu']

1706.02042v1

Face modeling has been paid much attention in the field of visual computing. There exist many scenarios, including cartoon characters, avatars for social media, 3D face caricatures as well as face-related art and design, where low-cost interactive face modeling is a popular approach especially among amateur users. In this paper, we propose a deep learning based sketching system for 3D face and caricature modeling. This system has a labor-efficient sketching interface, that allows the user to draw freehand imprecise yet expressive 2D lines representing the contours of facial features. A novel CNN based deep regression network is designed for inferring 3D face models from 2D sketches. Our network fuses both CNN and shape based features of the input sketch, and has two independent branches of fully connected layers generating independent subsets of coefficients for a bilinear face representation. Our system also supports gesture based interactions for users to further manipulate initial face models. Both user studies and numerical results indicate that our sketching system can help users create face models quickly and effectively. A significantly expanded face database with diverse identities, expressions and levels of exaggeration is constructed to promote further research and evaluation of face modeling techniques.

DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling

http://arxiv.org/pdf/1706.02042v1

Title DeepSketch2Face Deep Learning Based Sketching System 3D Face Caricature Modeling Summary Face modeling paid much attention field visual computing exist many scenario including cartoon character avatar social medium 3D face caricature well facerelated art design lowcost interactive face modeling popular approach especially among amateur user paper propose deep learning based sketching system 3D face caricature modeling system laborefficient sketching interface allows user draw freehand imprecise yet expressive 2D line representing contour facial feature novel CNN based deep regression network designed inferring 3D face model 2D sketch network fuse CNN shape based feature input sketch two independent branch fully connected layer generating independent subset coefficient bilinear face representation system also support gesture based interaction user manipulate initial face model user study numerical result indicate sketching system help user create face model quickly effectively significantly expanded face database diverse identity expression level exaggeration constructed promote research evaluation face modeling 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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['Wenqi Xian', 'Patsorn Sangkloy', 'Varun Agrawal', 'Amit Raj', 'Jingwan Lu', 'Chen Fang', 'Fisher Yu', 'James Hays']

1706.02823v2

In this paper, we investigate deep image synthesis guided by sketch, color, and texture. Previous image synthesis methods can be controlled by sketch and color strokes but we are the first to examine texture control. We allow a user to place a texture patch on a sketch at arbitrary locations and scales to control the desired output texture. Our generative network learns to synthesize objects consistent with these texture suggestions. To achieve this, we develop a local texture loss in addition to adversarial and content loss to train the generative network. We conduct experiments using sketches generated from real images and textures sampled from a separate texture database and results show that our proposed algorithm is able to generate plausible images that are faithful to user controls. Ablation studies show that our proposed pipeline can generate more realistic images than adapting existing methods directly.

TextureGAN: Controlling Deep Image Synthesis with Texture Patches

http://arxiv.org/pdf/1706.02823v2

Title TextureGAN Controlling Deep Image Synthesis Texture Patches Summary paper investigate deep image synthesis guided sketch color texture Previous image synthesis method controlled sketch color stroke first examine texture control allow user place texture patch sketch arbitrary location scale control desired output texture generative network learns synthesize object consistent texture suggestion achieve develop local texture loss addition adversarial content loss train generative network conduct experiment using sketch generated real image texture sampled separate texture database result show proposed algorithm able generate plausible image faithful user control Ablation study show proposed pipeline generate realistic image adapting existing method directly Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mikkel Kragh', 'James Underwood']

1706.02908v1

Reliable obstacle detection and classification in rough and unstructured terrain such as agricultural fields or orchards remains a challenging problem. These environments involve large variations in both geometry and appearance, challenging perception systems that rely on only a single sensor modality. Geometrically, tall grass, fallen leaves, or terrain roughness can mistakenly be perceived as non-traversable or might even obscure actual obstacles. Likewise, traversable grass or dirt roads and obstacles such as trees and bushes might be visually ambiguous. In this paper, we combine appearance- and geometry-based detection methods by probabilistically fusing lidar and camera sensing using a conditional random field. We apply a state-of-the-art multi-modal fusion algorithm from the scene analysis domain and adjust it for obstacle detection in agriculture with moving ground vehicles. This involves explicitly handling sparse point cloud data and exploiting both spatial, temporal, and multi-modal links between corresponding 2D and 3D regions. The proposed method is evaluated on a diverse dataset, comprising a dairy paddock and a number of different orchards gathered with a perception research robot in Australia. Results show that for a two-class classification problem (ground and non-ground), only the camera leverages from information provided by the other modality. However, as more classes are introduced (ground, sky, vegetation, and object), both modalities complement each other and improve the mean classification score. Further improvement is achieved by introducing recursive inference with temporal links between successive frames.

Multi-Modal Obstacle Detection in Unstructured Environments with Conditional Random Fields

http://arxiv.org/pdf/1706.02908v1

Title MultiModal Obstacle Detection Unstructured Environments Conditional Random Fields Summary Reliable obstacle detection classification rough unstructured terrain agricultural field orchard remains challenging problem environment involve large variation geometry appearance challenging perception system rely single sensor modality Geometrically tall grass fallen leaf terrain roughness mistakenly perceived nontraversable might even obscure actual obstacle Likewise traversable grass dirt road obstacle tree bush might visually ambiguous paper combine appearance geometrybased detection method probabilistically fusing lidar camera sensing using conditional random field apply stateoftheart multimodal fusion algorithm scene analysis domain adjust obstacle detection agriculture moving ground vehicle involves explicitly handling sparse point cloud data exploiting spatial temporal multimodal link corresponding 2D 3D region proposed method evaluated diverse dataset comprising dairy paddock number different orchard gathered perception research robot Australia Results show twoclass classification problem ground nonground camera leverage information provided modality However class introduced ground sky vegetation object modality complement improve mean classification score improvement achieved introducing recursive inference temporal link successive frame Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Fan Yang', 'Ajinkya Kale', 'Yury Bubnov', 'Leon Stein', 'Qiaosong Wang', 'Hadi Kiapour', 'Robinson Piramuthu']

1706.03154v2

In this paper, we propose a novel end-to-end approach for scalable visual search infrastructure. We discuss the challenges we faced for a massive volatile inventory like at eBay and present our solution to overcome those. We harness the availability of large image collection of eBay listings and state-of-the-art deep learning techniques to perform visual search at scale. Supervised approach for optimized search limited to top predicted categories and also for compact binary signature are key to scale up without compromising accuracy and precision. Both use a common deep neural network requiring only a single forward inference. The system architecture is presented with in-depth discussions of its basic components and optimizations for a trade-off between search relevance and latency. This solution is currently deployed in a distributed cloud infrastructure and fuels visual search in eBay ShopBot and Close5. We show benchmark on ImageNet dataset on which our approach is faster and more accurate than several unsupervised baselines. We share our learnings with the hope that visual search becomes a first class citizen for all large scale search engines rather than an afterthought.

Visual Search at eBay

http://arxiv.org/pdf/1706.03154v2

Title Visual Search eBay Summary paper propose novel endtoend approach scalable visual search infrastructure discus challenge faced massive volatile inventory like eBay present solution overcome harness availability large image collection eBay listing stateoftheart deep learning technique perform visual search scale Supervised approach optimized search limited top predicted category also compact binary signature key scale without compromising accuracy precision use common deep neural network requiring single forward inference system architecture presented indepth discussion basic component optimization tradeoff search relevance latency solution currently deployed distributed cloud infrastructure fuel visual search eBay ShopBot Close5 show benchmark ImageNet dataset approach faster accurate several unsupervised baseline share learning hope visual search becomes first class citizen large scale search engine rather afterthought Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Aseem Saxena', 'Harit Pandya', 'Gourav Kumar', 'Ayush Gaud', 'K. Madhava Krishna']

1706.03220v1

Present image based visual servoing approaches rely on extracting hand crafted visual features from an image. Choosing the right set of features is important as it directly affects the performance of any approach. Motivated by recent breakthroughs in performance of data driven methods on recognition and localization tasks, we aim to learn visual feature representations suitable for servoing tasks in unstructured and unknown environments. In this paper, we present an end-to-end learning based approach for visual servoing in diverse scenes where the knowledge of camera parameters and scene geometry is not available a priori. This is achieved by training a convolutional neural network over color images with synchronised camera poses. Through experiments performed in simulation and on a quadrotor, we demonstrate the efficacy and robustness of our approach for a wide range of camera poses in both indoor as well as outdoor environments.

Exploring Convolutional Networks for End-to-End Visual Servoing

http://arxiv.org/pdf/1706.03220v1

Title Exploring Convolutional Networks EndtoEnd Visual Servoing Summary Present image based visual servoing approach rely extracting hand crafted visual feature image Choosing right set feature important directly affect performance approach Motivated recent breakthrough performance data driven method recognition localization task aim learn visual feature representation suitable servoing task unstructured unknown environment paper present endtoend learning based approach visual servoing diverse scene knowledge camera parameter scene geometry available priori achieved training convolutional neural network color image synchronised camera pose experiment performed simulation quadrotor demonstrate efficacy robustness approach wide range camera pose indoor well outdoor environment Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Alexandre Fioravante de Siqueira', 'Wagner Massayuki Nakasuga', 'Sandro Guedes', 'Lothar Ratschbacher']

1706.03282v2

The major challenges of automatic track counting are distinguishing tracks and material defects, identifying small tracks and defects of similar size, and detecting overlapping tracks. Here we address the latter issue using WUSEM, an algorithm which combines the watershed transform, morphological erosions and labeling to separate regions in photomicrographs. WUSEM shows reliable results when used in photomicrographs presenting almost isotropic objects. We tested this method in two datasets of diallyl phthalate (DAP) photomicrographs and compared the results when counting manually and using the classic watershed. The mean automatic/manual efficiency ratio when using WUSEM in the test datasets is 0.97 +/- 0.11.

Segmentation of nearly isotropic overlapped tracks in photomicrographs using successive erosions as watershed markers

http://arxiv.org/pdf/1706.03282v2

Title Segmentation nearly isotropic overlapped track photomicrograph using successive erosion watershed marker Summary major challenge automatic track counting distinguishing track material defect identifying small track defect similar size detecting overlapping track address latter issue using WUSEM algorithm combine watershed transform morphological erosion labeling separate region photomicrograph WUSEM show reliable result used photomicrograph presenting almost isotropic object tested method two datasets diallyl phthalate DAP photomicrograph compared result counting manually using classic watershed mean automaticmanual efficiency ratio using WUSEM test datasets 097 011 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yuichi Yagi']

1706.03497v1

We present a filter based approach for inbetweening. We train a convolutional neural network to generate intermediate frames. This network aim to generate smooth animation of line drawings. Our method can process scanned images directly. Our method does not need to compute correspondence of lines and topological changes explicitly. We experiment our method with real animation production data. The results show that our method can generate intermediate frames partially.

A filter based approach for inbetweening

http://arxiv.org/pdf/1706.03497v1

Title filter based approach inbetweening Summary present filter based approach inbetweening train convolutional neural network generate intermediate frame network aim generate smooth animation line drawing method process scanned image directly method need compute correspondence line topological change explicitly experiment method real animation production data result show method generate intermediate frame partially Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Bartlomiej Placzek', 'Jolnta Golosz']

1706.03699v1

The paper presents the vehicles integrated monitoring system giving priorities for emergency vehicles. The described system exploits the data gathered by: geographical positioning systems and geographical information systems. The digital maps and roadside cameras provide the dispatchers with aims for in town ambulances traffic management. The method of vehicles positioning in the city network and algorithms for ambulances recognition by image processing techniques have been discussed in the paper. These priorities are needed for an efficient life-saving actions that require the real-time controlling strategies.

The in-town monitoring system for ambulance dispatch centre

http://arxiv.org/pdf/1706.03699v1

Title intown monitoring system ambulance dispatch centre Summary paper present vehicle integrated monitoring system giving priority emergency vehicle described system exploit data gathered geographical positioning system geographical information system digital map roadside camera provide dispatcher aim town ambulance traffic management method vehicle positioning city network algorithm ambulance recognition image processing technique discussed paper priority needed efficient lifesaving action require realtime controlling strategy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['J. S. Hammudoglu', 'J. Sparreboom', 'J. I. Rauhamaa', 'J. K. Faber', 'L. C. Guerchi', 'I. P. Samiotis', 'S. P. Rao', 'J. A. Pouwelse']

1706.03744v1

We devised a mobile biometric-based authentication system only relying on local processing. Our Android open source solution explores the capability of current smartphones to acquire, process and match fingerprints using only its built-in hardware. Our architecture is specifically designed to run completely locally and autonomously, not requiring any cloud service, server, or permissioned access to fingerprint reader hardware. It involves three main stages, starting with the fingerprint acquisition using the smartphone camera, followed by a processing pipeline to obtain minutiae features and a final step for matching against other locally stored fingerprints, based on Oriented FAST and Rotated BRIEF (ORB) descriptors. We obtained a mean matching accuracy of 55%, with the highest value of 67% for thumb fingers. Our ability to capture and process a finger fingerprint in mere seconds using a smartphone makes this work usable in a wide range of scenarios, for instance, offline remote regions. This work is specifically designed to be a key building block for a self-sovereign identity solution and integrate with our permissionless blockchain for identity and key attestation.

Portable Trust: biometric-based authentication and blockchain storage for self-sovereign identity systems

http://arxiv.org/pdf/1706.03744v1

Title Portable Trust biometricbased authentication blockchain storage selfsovereign identity system Summary devised mobile biometricbased authentication system relying local processing Android open source solution explores capability current smartphones acquire process match fingerprint using builtin hardware architecture specifically designed run completely locally autonomously requiring cloud service server permissioned access fingerprint reader hardware involves three main stage starting fingerprint acquisition using smartphone camera followed processing pipeline obtain minutia feature final step matching locally stored fingerprint based Oriented FAST Rotated BRIEF ORB descriptor obtained mean matching accuracy 55 highest value 67 thumb finger ability capture process finger fingerprint mere second using smartphone make work usable wide range scenario instance offline remote region work specifically designed key building block selfsovereign identity solution integrate permissionless blockchain identity key attestation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Pedro F. Proenca', 'Yang Gao']

1706.04034v3

This work proposes a robust visual odometry method for structured environments that combines point features with line and plane segments, extracted through an RGB-D camera. Noisy depth maps are processed by a probabilistic depth fusion framework based on Mixtures of Gaussians to denoise and derive the depth uncertainty, which is then propagated throughout the visual odometry pipeline. Probabilistic 3D plane and line fitting solutions are used to model the uncertainties of the feature parameters and pose is estimated by combining the three types of primitives based on their uncertainties. Performance evaluation on RGB-D sequences collected in this work and two public RGB-D datasets: TUM and ICL-NUIM show the benefit of using the proposed depth fusion framework and combining the three feature-types, particularly in scenes with low-textured surfaces, dynamic objects and missing depth measurements.

Probabilistic RGB-D Odometry based on Points, Lines and Planes Under Depth Uncertainty

http://arxiv.org/pdf/1706.04034v3

Title Probabilistic RGBD Odometry based Points Lines Planes Depth Uncertainty Summary work proposes robust visual odometry method structured environment combine point feature line plane segment extracted RGBD camera Noisy depth map processed probabilistic depth fusion framework based Mixtures Gaussians denoise derive depth uncertainty propagated throughout visual odometry pipeline Probabilistic 3D plane line fitting solution used model uncertainty feature parameter pose estimated combining three type primitive based uncertainty Performance evaluation RGBD sequence collected work two public RGBD datasets TUM ICLNUIM show benefit using proposed depth fusion framework combining three featuretypes particularly scene lowtextured surface dynamic object missing depth measurement Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Roger Gomez Nieto', 'Andres Marino Alvarez Meza', 'Julian David Echeverry Correa', 'Alvaro Angel Orozco Gutierrez']

1706.04254v1

Parkinson's disease (PD) is a degenerative condition of the nervous system, which manifests itself primarily as muscle stiffness, hypokinesia, bradykinesia, and tremor. In patients suffering from advanced stages of PD, Deep Brain Stimulation neurosurgery (DBS) is the best alternative to medical treatment, especially when they become tolerant to the drugs. This surgery produces a neuronal activity, a result from electrical stimulation, whose quantification is known as Volume of Tissue Activated (VTA). To locate correctly the VTA in the cerebral volume space, one should be aware exactly the location of the tip of the DBS electrodes, as well as their spatial projection. In this paper, we automatically locate DBS electrodes using a threshold-based medical imaging segmentation methodology, determining the optimal value of this threshold adaptively. The proposed methodology allows the localization of DBS electrodes in Computed Tomography (CT) images, with high noise tolerance, using automatic threshold detection methods.

Automatic Localization of Deep Stimulation Electrodes Using Trajectory-based Segmentation Approach

http://arxiv.org/pdf/1706.04254v1

Title Automatic Localization Deep Stimulation Electrodes Using Trajectorybased Segmentation Approach Summary Parkinsons disease PD degenerative condition nervous system manifest primarily muscle stiffness hypokinesia bradykinesia tremor patient suffering advanced stage PD Deep Brain Stimulation neurosurgery DBS best alternative medical treatment especially become tolerant drug surgery produce neuronal activity result electrical stimulation whose quantification known Volume Tissue Activated VTA locate correctly VTA cerebral volume space one aware exactly location tip DBS electrode well spatial projection paper automatically locate DBS electrode using thresholdbased medical imaging segmentation methodology determining optimal value threshold adaptively proposed methodology allows localization DBS electrode Computed Tomography CT image high noise tolerance using automatic threshold detection 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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['Haibin Huang', 'Evangelos Kalogerakis', 'Siddhartha Chaudhuri', 'Duygu Ceylan', 'Vladimir G. Kim', 'Ersin Yumer']

1706.04496v2

We present a new local descriptor for 3D shapes, directly applicable to a wide range of shape analysis problems such as point correspondences, semantic segmentation, affordance prediction, and shape-to-scan matching. The descriptor is produced by a convolutional network that is trained to embed geometrically and semantically similar points close to one another in descriptor space. The network processes surface neighborhoods around points on a shape that are captured at multiple scales by a succession of progressively zoomed out views, taken from carefully selected camera positions. We leverage two extremely large sources of data to train our network. First, since our network processes rendered views in the form of 2D images, we repurpose architectures pre-trained on massive image datasets. Second, we automatically generate a synthetic dense point correspondence dataset by non-rigid alignment of corresponding shape parts in a large collection of segmented 3D models. As a result of these design choices, our network effectively encodes multi-scale local context and fine-grained surface detail. Our network can be trained to produce either category-specific descriptors or more generic descriptors by learning from multiple shape categories. Once trained, at test time, the network extracts local descriptors for shapes without requiring any part segmentation as input. Our method can produce effective local descriptors even for shapes whose category is unknown or different from the ones used while training. We demonstrate through several experiments that our learned local descriptors are more discriminative compared to state of the art alternatives, and are effective in a variety of shape analysis applications.

Learning Local Shape Descriptors from Part Correspondences With Multi-view Convolutional Networks

http://arxiv.org/pdf/1706.04496v2

Title Learning Local Shape Descriptors Part Correspondences Multiview Convolutional Networks Summary present new local descriptor 3D shape directly applicable wide range shape analysis problem point correspondence semantic segmentation affordance prediction shapetoscan matching descriptor produced convolutional network trained embed geometrically semantically similar point close one another descriptor space network process surface neighborhood around point shape captured multiple scale succession progressively zoomed view taken carefully selected camera position leverage two extremely large source data train network First since network process rendered view form 2D image repurpose architecture pretrained massive image datasets Second automatically generate synthetic dense point correspondence dataset nonrigid alignment corresponding shape part large collection segmented 3D model result design choice network effectively encodes multiscale local context finegrained surface detail network trained produce either categoryspecific descriptor generic descriptor learning multiple shape category trained test time network extract local descriptor shape without requiring part segmentation input method produce effective local descriptor even shape whose category unknown different one used training demonstrate several experiment learned local descriptor discriminative compared state art alternative effective variety shape analysis 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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['Yu-Gang Jiang', 'Zuxuan Wu', 'Jinhui Tang', 'Zechao Li', 'Xiangyang Xue', 'Shih-Fu Chang']

1706.04508v1

Videos are inherently multimodal. This paper studies the problem of how to fully exploit the abundant multimodal clues for improved video categorization. We introduce a hybrid deep learning framework that integrates useful clues from multiple modalities, including static spatial appearance information, motion patterns within a short time window, audio information as well as long-range temporal dynamics. More specifically, we utilize three Convolutional Neural Networks (CNNs) operating on appearance, motion and audio signals to extract their corresponding features. We then employ a feature fusion network to derive a unified representation with an aim to capture the relationships among features. Furthermore, to exploit the long-range temporal dynamics in videos, we apply two Long Short Term Memory networks with extracted appearance and motion features as inputs. Finally, we also propose to refine the prediction scores by leveraging contextual relationships among video semantics. The hybrid deep learning framework is able to exploit a comprehensive set of multimodal features for video classification. Through an extensive set of experiments, we demonstrate that (1) LSTM networks which model sequences in an explicitly recurrent manner are highly complementary with CNN models; (2) the feature fusion network which produces a fused representation through modeling feature relationships outperforms alternative fusion strategies; (3) the semantic context of video classes can help further refine the predictions for improved performance. Experimental results on two challenging benchmarks, the UCF-101 and the Columbia Consumer Videos (CCV), provide strong quantitative evidence that our framework achieves promising results: $93.1\%$ on the UCF-101 and $84.5\%$ on the CCV, outperforming competing methods with clear margins.

Modeling Multimodal Clues in a Hybrid Deep Learning Framework for Video Classification

http://arxiv.org/pdf/1706.04508v1

Title Modeling Multimodal Clues Hybrid Deep Learning Framework Video Classification Summary Videos inherently multimodal paper study problem fully exploit abundant multimodal clue improved video categorization introduce hybrid deep learning framework integrates useful clue multiple modality including static spatial appearance information motion pattern within short time window audio information well longrange temporal dynamic specifically utilize three Convolutional Neural Networks CNNs operating appearance motion audio signal extract corresponding feature employ feature fusion network derive unified representation aim capture relationship among feature Furthermore exploit longrange temporal dynamic video apply two Long Short Term Memory network extracted appearance motion feature input Finally also propose refine prediction score leveraging contextual relationship among video semantics hybrid deep learning framework able exploit comprehensive set multimodal feature video classification extensive set experiment demonstrate 1 LSTM network model sequence explicitly recurrent manner highly complementary CNN model 2 feature fusion network produce fused representation modeling feature relationship outperforms alternative fusion strategy 3 semantic context video class help refine prediction improved performance Experimental result two challenging benchmark UCF101 Columbia Consumer Videos CCV provide strong quantitative evidence framework achieves promising result 931 UCF101 845 CCV outperforming competing method clear margin Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Madhuri Suthar', 'Mohammad Asghari', 'Bahram Jalali']

1706.04671v1

One of the major open problems in computer vision is detection of features in visually impaired images. In this paper, we describe a potential solution using Phase Stretch Transform, a new computational approach for image analysis, edge detection and resolution enhancement that is inspired by the physics of the photonic time stretch technique. We mathematically derive the intrinsic nonlinear transfer function and demonstrate how it leads to (1) superior performance at low contrast levels and (2) a reconfigurable operator for hyper-dimensional classification. We prove that the Phase Stretch Transform equalizes the input image brightness across the range of intensities resulting in a high dynamic range in visually impaired images. We also show further improvement in the dynamic range by combining our method with the conventional techniques. Finally, our results show a method for computation of mathematical derivatives via group delay dispersion operations.

Feature Enhancement in Visually Impaired Images

http://arxiv.org/pdf/1706.04671v1

Title Feature Enhancement Visually Impaired Images Summary One major open problem computer vision detection feature visually impaired image paper describe potential solution using Phase Stretch Transform new computational approach image analysis edge detection resolution enhancement inspired physic photonic time stretch technique mathematically derive intrinsic nonlinear transfer function demonstrate lead 1 superior performance low contrast level 2 reconfigurable operator hyperdimensional classification prove Phase Stretch Transform equalizes input image brightness across range intensity resulting high dynamic range visually impaired image also show improvement dynamic range combining method conventional technique Finally result show method computation mathematical derivative via group delay dispersion 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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['Xiangrui Zeng', 'Miguel Ricardo Leung', 'Tzviya Zeev-Ben-Mordehai', 'Min Xu']

1706.04970v2

Cellular electron cryo-tomography enables the 3D visualization of cellular organization in the near-native state and at submolecular resolution. However, the contents of cellular tomograms are often complex, making it difficult to automatically isolate different in situ cellular components. In this paper, we propose a convolutional autoencoder-based unsupervised approach to provide a coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate that the autoencoder can be used for efficient and coarse characterization of features of macromolecular complexes and surfaces, such as membranes. In addition, the autoencoder can be used to detect non-cellular features related to sample preparation and data collection, such as carbon edges from the grid and tomogram boundaries. The autoencoder is also able to detect patterns that may indicate spatial interactions between cellular components. Furthermore, we demonstrate that our autoencoder can be used for weakly supervised semantic segmentation of cellular components, requiring a very small amount of manual annotation.

A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation

http://arxiv.org/pdf/1706.04970v2

Title convolutional autoencoder approach mining feature cellular electron cryotomograms weakly supervised coarse segmentation Summary Cellular electron cryotomography enables 3D visualization cellular organization nearnative state submolecular resolution However content cellular tomograms often complex making difficult automatically isolate different situ cellular component paper propose convolutional autoencoderbased unsupervised approach provide coarse grouping 3D small subvolumes extracted tomograms demonstrate autoencoder used efficient coarse characterization feature macromolecular complex surface membrane addition autoencoder used detect noncellular feature related sample preparation data collection carbon edge grid tomogram boundary autoencoder also able detect pattern may indicate spatial interaction cellular component Furthermore demonstrate autoencoder used weakly supervised semantic segmentation cellular component requiring small amount manual annotation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jerry Liu', 'Fisher Yu', 'Thomas Funkhouser']

1706.05170v2

This paper proposes the idea of using a generative adversarial network (GAN) to assist a novice user in designing real-world shapes with a simple interface. The user edits a voxel grid with a painting interface (like Minecraft). Yet, at any time, he/she can execute a SNAP command, which projects the current voxel grid onto a latent shape manifold with a learned projection operator and then generates a similar, but more realistic, shape using a learned generator network. Then the user can edit the resulting shape and snap again until he/she is satisfied with the result. The main advantage of this approach is that the projection and generation operators assist novice users to create 3D models characteristic of a background distribution of object shapes, but without having to specify all the details. The core new research idea is to use a GAN to support this application. 3D GANs have previously been used for shape generation, interpolation, and completion, but never for interactive modeling. The new challenge for this application is to learn a projection operator that takes an arbitrary 3D voxel model and produces a latent vector on the shape manifold from which a similar and realistic shape can be generated. We develop algorithms for this and other steps of the SNAP processing pipeline and integrate them into a simple modeling tool. Experiments with these algorithms and tool suggest that GANs provide a promising approach to computer-assisted interactive modeling.

Interactive 3D Modeling with a Generative Adversarial Network

http://arxiv.org/pdf/1706.05170v2

Title Interactive 3D Modeling Generative Adversarial Network Summary paper proposes idea using generative adversarial network GAN assist novice user designing realworld shape simple interface user edits voxel grid painting interface like Minecraft Yet time heshe execute SNAP command project current voxel grid onto latent shape manifold learned projection operator generates similar realistic shape using learned generator network user edit resulting shape snap heshe satisfied result main advantage approach projection generation operator assist novice user create 3D model characteristic background distribution object shape without specify detail core new research idea use GAN support application 3D GANs previously used shape generation interpolation completion never interactive modeling new challenge application learn projection operator take arbitrary 3D voxel model produce latent vector shape manifold similar realistic shape generated develop algorithm step SNAP processing pipeline integrate simple modeling tool Experiments algorithm tool suggest GANs provide promising approach computerassisted interactive modeling Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['David Estevez', 'Juan G. Victores', 'Raul Fernandez-Fernandez', 'Carlos Balaguer']

1706.05340v1

As robotic systems become more popular in household environments, the complexity of required tasks also increases. In this work we focus on a domestic chore deemed dull by a majority of the population, the task of ironing. The presented algorithm improves on the limited number of previous works by joining 3D perception with force/torque sensing, with emphasis on finding a practical solution with a feasible implementation in a domestic setting. Our algorithm obtains a point cloud representation of the working environment. From this point cloud, the garment is segmented and a custom Wrinkleness Local Descriptor (WiLD) is computed to determine the location of the present wrinkles. Using this descriptor, the most suitable ironing path is computed and, based on it, the manipulation algorithm performs the force-controlled ironing operation. Experiments have been performed with a humanoid robot platform, proving that our algorithm is able to detect successfully wrinkles present in garments and iteratively reduce the wrinkleness using an unmodified iron.

Robotic Ironing with 3D Perception and Force/Torque Feedback in Household Environments

http://arxiv.org/pdf/1706.05340v1

Title Robotic Ironing 3D Perception ForceTorque Feedback Household Environments Summary robotic system become popular household environment complexity required task also increase work focus domestic chore deemed dull majority population task ironing presented algorithm improves limited number previous work joining 3D perception forcetorque sensing emphasis finding practical solution feasible implementation domestic setting algorithm obtains point cloud representation working environment point cloud garment segmented custom Wrinkleness Local Descriptor WiLD computed determine location present wrinkle Using descriptor suitable ironing path computed based manipulation algorithm performs forcecontrolled ironing operation Experiments performed humanoid robot platform proving algorithm able detect successfully wrinkle present garment iteratively reduce wrinkleness using unmodified iron Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Pablo Messina', 'Vicente Dominguez', 'Denis Parra', 'Christoph Trattner', 'Alvaro Soto']

1706.05786v3

Compared to other areas, artwork recommendation has received little attention, despite the continuous growth of the artwork market. Previous research has relied on ratings and metadata to make artwork recommendations, as well as visual features extracted with deep neural networks (DNN). However, these features have no direct interpretation to explicit visual features (e.g. brightness, texture) which might hinder explainability and user-acceptance. In this work, we study the impact of artwork metadata as well as visual features (DNN-based and attractiveness-based) for physical artwork recommendation, using images and transaction data from the UGallery online artwork store. Our results indicate that: (i) visual features perform better than manually curated data, (ii) DNN-based visual features perform better than attractiveness-based ones, and (iii) a hybrid approach improves the performance further. Our research can inform the development of new artwork recommenders relying on diverse content data.

Exploring Content-based Artwork Recommendation with Metadata and Visual Features

http://arxiv.org/pdf/1706.05786v3

Title Exploring Contentbased Artwork Recommendation Metadata Visual Features Summary Compared area artwork recommendation received little attention despite continuous growth artwork market Previous research relied rating metadata make artwork recommendation well visual feature extracted deep neural network DNN However feature direct interpretation explicit visual feature eg brightness texture might hinder explainability useracceptance work study impact artwork metadata well visual feature DNNbased attractivenessbased physical artwork recommendation using image transaction data UGallery online artwork store result indicate visual feature perform better manually curated data ii DNNbased visual feature perform better attractivenessbased one iii hybrid approach improves performance research inform development new artwork recommenders relying diverse content 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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['Michael Burke']

1706.05850v1

This paper describes a novel storyboarding scheme that uses a model trained on pairwise image comparisons to identify images likely to be of interest to a mobile robot user. Traditional storyboarding schemes typically attempt to summarise robot observations using predefined novelty or image quality objectives, but we propose a user training stage that allows the incorporation of user interest when storyboarding. Our approach dramatically reduces the number of image comparisons required to infer image interest by applying a Gaussian process smoothing algorithm on image features extracted using a pre-trained convolutional neural network. As a particularly valuable by-product, the proposed approach allows the generation of user-specific saliency or attention maps.

User-driven mobile robot storyboarding: Learning image interest and saliency from pairwise image comparisons

http://arxiv.org/pdf/1706.05850v1

Title Userdriven mobile robot storyboarding Learning image interest saliency pairwise image comparison Summary paper describes novel storyboarding scheme us model trained pairwise image comparison identify image likely interest mobile robot user Traditional storyboarding scheme typically attempt summarise robot observation using predefined novelty image quality objective propose user training stage allows incorporation user interest storyboarding approach dramatically reduces number image comparison required infer image interest applying Gaussian process smoothing algorithm image feature extracted using pretrained convolutional neural network particularly valuable byproduct proposed approach allows generation userspecific saliency attention 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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['Hosnieh Sattar', 'Mario Fritz', 'Andreas Bulling']

1706.05993v3

What does human gaze reveal about a users' intents and to which extend can these intents be inferred or even visualized? Gaze was proposed as an implicit source of information to predict the target of visual search and, more recently, to predict the object class and attributes of the search target. In this work, we go one step further and investigate the feasibility of combining recent advances in encoding human gaze information using deep convolutional neural networks with the power of generative image models to visually decode, i.e. create a visual representation of, the search target. Such visual decoding is challenging for two reasons: 1) the search target only resides in the user's mind as a subjective visual pattern, and can most often not even be described verbally by the person, and 2) it is, as of yet, unclear if gaze fixations contain sufficient information for this task at all. We show, for the first time, that visual representations of search targets can indeed be decoded only from human gaze fixations. We propose to first encode fixations into a semantic representation and then decode this representation into an image. We evaluate our method on a recent gaze dataset of 14 participants searching for clothing in image collages and validate the model's predictions using two human studies. Our results show that 62% (Chance level = 10%) of the time users were able to select the categories of the decoded image right. In our second studies we show the importance of a local gaze encoding for decoding visual search targets of user

Visual Decoding of Targets During Visual Search From Human Eye Fixations

http://arxiv.org/pdf/1706.05993v3

Title Visual Decoding Targets Visual Search Human Eye Fixations Summary human gaze reveal user intent extend intent inferred even visualized Gaze proposed implicit source information predict target visual search recently predict object class attribute search target work go one step investigate feasibility combining recent advance encoding human gaze information using deep convolutional neural network power generative image model visually decode ie create visual representation search target visual decoding challenging two reason 1 search target resides user mind subjective visual pattern often even described verbally person 2 yet unclear gaze fixation contain sufficient information task show first time visual representation search target indeed decoded human gaze fixation propose first encode fixation semantic representation decode representation image evaluate method recent gaze dataset 14 participant searching clothing image collage validate model prediction using two human study result show 62 Chance level 10 time user able select category decoded image right second study show importance local gaze encoding decoding visual search target user Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Dmitry Petrov', 'Alexander Ivanov', 'Joshua Faskowitz', 'Boris Gutman', 'Daniel Moyer', 'Julio Villalon', 'Neda Jahanshad', 'Paul Thompson']

1706.06031v1

There is no consensus on how to construct structural brain networks from diffusion MRI. How variations in pre-processing steps affect network reliability and its ability to distinguish subjects remains opaque. In this work, we address this issue by comparing 35 structural connectome-building pipelines. We vary diffusion reconstruction models, tractography algorithms and parcellations. Next, we classify structural connectome pairs as either belonging to the same individual or not. Connectome weights and eight topological derivative measures form our feature set. For experiments, we use three test-retest datasets from the Consortium for Reliability and Reproducibility (CoRR) comprised of a total of 105 individuals. We also compare pairwise classification results to a commonly used parametric test-retest measure, Intraclass Correlation Coefficient (ICC).

Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification

http://arxiv.org/pdf/1706.06031v1

Title Evaluating 35 Methods Generate Structural Connectomes Using Pairwise Classification Summary consensus construct structural brain network diffusion MRI variation preprocessing step affect network reliability ability distinguish subject remains opaque work address issue comparing 35 structural connectomebuilding pipeline vary diffusion reconstruction model tractography algorithm parcellation Next classify structural connectome pair either belonging individual Connectome weight eight topological derivative measure form feature set experiment use three testretest datasets Consortium Reliability Reproducibility CoRR comprised total 105 individual also compare pairwise classification result commonly used parametric testretest measure Intraclass Correlation Coefficient ICC Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['William F. Kindel', 'Elijah D. Christensen', 'Joel Zylberberg']

1706.06208v1

Primary visual cortex (V1) is the first stage of cortical image processing, and a major effort in systems neuroscience is devoted to understanding how it encodes information about visual stimuli. Within V1, many neurons respond selectively to edges of a given preferred orientation: these are known as simple or complex cells, and they are well-studied. Other neurons respond to localized center-surround image features. Still others respond selectively to certain image stimuli, but the specific features that excite them are unknown. Moreover, even for the simple and complex cells-- the best-understood V1 neurons-- it is challenging to predict how they will respond to natural image stimuli. Thus, there are important gaps in our understanding of how V1 encodes images. To fill this gap, we train deep convolutional neural networks to predict the firing rates of V1 neurons in response to natural image stimuli, and find that 15% of these neurons are within 10% of their theoretical limit of predictability. For these well predicted neurons, we invert the predictor network to identify the image features (receptive fields) that cause the V1 neurons to spike. In addition to those with previously-characterized receptive fields (Gabor wavelet and center-surround), we identify neurons that respond predictably to higher-level textural image features that are not localized to any particular region of the image.

Using deep learning to reveal the neural code for images in primary visual cortex

http://arxiv.org/pdf/1706.06208v1

Title Using deep learning reveal neural code image primary visual cortex Summary Primary visual cortex V1 first stage cortical image processing major effort system neuroscience devoted understanding encodes information visual stimulus Within V1 many neuron respond selectively edge given preferred orientation known simple complex cell wellstudied neuron respond localized centersurround image feature Still others respond selectively certain image stimulus specific feature excite unknown Moreover even simple complex cell bestunderstood V1 neuron challenging predict respond natural image stimulus Thus important gap understanding V1 encodes image fill gap train deep convolutional neural network predict firing rate V1 neuron response natural image stimulus find 15 neuron within 10 theoretical limit predictability well predicted neuron invert predictor network identify image feature receptive field cause V1 neuron spike addition previouslycharacterized receptive field Gabor wavelet centersurround identify neuron respond predictably higherlevel textural image feature localized particular region 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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['Neha Rani', 'Sharda Vashisth']

1706.06411v1

Brain is an organ that controls activities of all the parts of the body. Recognition of automated brain tumor in Magnetic resonance imaging (MRI) is a difficult task due to complexity of size and location variability. This automatic method detects all the type of cancer present in the body. Previous methods for tumor are time consuming and less accurate. In the present work, statistical analysis morphological and thresholding techniques are used to process the images obtained by MRI. Feed-forward back-prop neural network is used to classify the performance of tumors part of the image. This method results high accuracy and less iterations detection which further reduces the consumption time.

Brain Tumor Detection and Classification with Feed Forward Back-Prop Neural Network

http://arxiv.org/pdf/1706.06411v1

Title Brain Tumor Detection Classification Feed Forward BackProp Neural Network Summary Brain organ control activity part body Recognition automated brain tumor Magnetic resonance imaging MRI difficult task due complexity size location variability automatic method detects type cancer present body Previous method tumor time consuming le accurate present work statistical analysis morphological thresholding technique used process image obtained MRI Feedforward backprop neural network used classify performance tumor part image method result high accuracy le iteration detection reduces consumption time Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Seyed Majid Azimi', 'Dominik Britz', 'Michael Engstler', 'Mario Fritz', 'Frank Mücklich']

1706.06480v2

The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which gives rise to uncertainties due to subjectivity. Since the microstructure could be a combination of different phases or constituents with complex substructures its automatic classification is very challenging and only a few prior studies exist. Prior works focused on designed and engineered features by experts and classified microstructures separately from the feature extraction step. Recently, Deep Learning methods have shown strong performance in vision applications by learning the features from data together with the classification step. In this work, we propose a Deep Learning method for microstructural classification in the examples of certain microstructural constituents of low carbon steel. This novel method employs pixel-wise segmentation via Fully Convolutional Neural Networks (FCNN) accompanied by a max-voting scheme. Our system achieves 93.94% classification accuracy, drastically outperforming the state-of-the-art method of 48.89% accuracy. Beyond the strong performance of our method, this line of research offers a more robust and first of all objective way for the difficult task of steel quality appreciation.

Advanced Steel Microstructural Classification by Deep Learning Methods

http://arxiv.org/pdf/1706.06480v2

Title Advanced Steel Microstructural Classification Deep Learning Methods Summary inner structure material called microstructure store genesis material determines physical chemical property microstructural characterization widely spread well known microstructural classification mostly done manually human expert give rise uncertainty due subjectivity Since microstructure could combination different phase constituent complex substructure automatic classification challenging prior study exist Prior work focused designed engineered feature expert classified microstructures separately feature extraction step Recently Deep Learning method shown strong performance vision application learning feature data together classification step work propose Deep Learning method microstructural classification example certain microstructural constituent low carbon steel novel method employ pixelwise segmentation via Fully Convolutional Neural Networks FCNN accompanied maxvoting scheme system achieves 9394 classification accuracy drastically outperforming stateoftheart method 4889 accuracy Beyond strong performance method line research offer robust first objective way difficult task steel quality appreciation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hardik Jain', 'Gaurav Gupta', 'Sharad Joshi', 'Nitin Khanna']

1706.06651v1

In this digital era, one thing that still holds the convention is a printed archive. Printed documents find their use in many critical domains such as contract papers, legal tenders and proof of identity documents. As more advanced printing, scanning and image editing techniques are becoming available, forgeries on these legal tenders pose a serious threat. Ability to easily and reliably identify source printer of a printed document can help a lot in reducing this menace. During printing procedure, printer hardware introduces certain distortions in printed characters' locations and shapes which are invisible to naked eyes. These distortions are referred as geometric distortions, their profile (or signature) is generally unique for each printer and can be used for printer classification purpose. This paper proposes a set of features for characterizing text-line-level geometric distortions, referred as geometric distortion signatures and presents a novel system to use them for identification of the origin of a printed document. Detailed experiments performed on a set of thirteen printers demonstrate that the proposed system achieves state of the art performance and gives much higher accuracy under small training size constraint. For four training and six test pages of three different fonts, the proposed method gives 99\% classification accuracy.

Passive Classification of Source Printer using Text-line-level Geometric Distortion Signatures from Scanned Images of Printed Documents

http://arxiv.org/pdf/1706.06651v1

Title Passive Classification Source Printer using Textlinelevel Geometric Distortion Signatures Scanned Images Printed Documents Summary digital era one thing still hold convention printed archive Printed document find use many critical domain contract paper legal tender proof identity document advanced printing scanning image editing technique becoming available forgery legal tender pose serious threat Ability easily reliably identify source printer printed document help lot reducing menace printing procedure printer hardware introduces certain distortion printed character location shape invisible naked eye distortion referred geometric distortion profile signature generally unique printer used printer classification purpose paper proposes set feature characterizing textlinelevel geometric distortion referred geometric distortion signature present novel system use identification origin printed document Detailed experiment performed set thirteen printer demonstrate proposed system achieves state art performance give much higher accuracy small training size constraint four training six test page three different font proposed method give 99 classification accuracy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Luz María Martínez', 'Javier Ruiz-del-Solar']

1706.06694v1

In this work a system for recognizing grasp points in RGB-D images is proposed. This system is intended to be used by a domestic robot when deploying clothes lying at a random position on a table. By taking into consideration that the grasp points are usually near key parts of clothing, such as the waist of pants or the neck of a shirt. The proposed system attempts to detect these key parts first, using a local multivariate contour that adapts its shape accordingly. Then, the proposed system applies the Vessel Enhancement filter to identify wrinkles in the clothes, allowing to compute a roughness index for the clothes. Finally, by mixing (i) the key part contours and (ii) the roughness information obtained by the vessel filter, the system is able to recognize grasp points for unfolding a piece of clothing. The recognition system is validated using realistic RGB-D images of different cloth types.

Recognition of Grasp Points for Clothes Manipulation under unconstrained Conditions

http://arxiv.org/pdf/1706.06694v1

Title Recognition Grasp Points Clothes Manipulation unconstrained Conditions Summary work system recognizing grasp point RGBD image proposed system intended used domestic robot deploying clothes lying random position table taking consideration grasp point usually near key part clothing waist pant neck shirt proposed system attempt detect key part first using local multivariate contour adapts shape accordingly proposed system applies Vessel Enhancement filter identify wrinkle clothes allowing compute roughness index clothes Finally mixing key part contour ii roughness information obtained vessel filter system able recognize grasp point unfolding piece clothing recognition system validated using realistic RGBD image different cloth type Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sean McMahon', 'Niko Sünderhauf', 'Ben Upcroft', 'Michael Milford']

1706.06718v1

Trip hazards are a significant contributor to accidents on construction and manufacturing sites, where over a third of Australian workplace injuries occur [1]. Current safety inspections are labour intensive and limited by human fallibility,making automation of trip hazard detection appealing from both a safety and economic perspective. Trip hazards present an interesting challenge to modern learning techniques because they are defined as much by affordance as by object type; for example wires on a table are not a trip hazard, but can be if lying on the ground. To address these challenges, we conduct a comprehensive investigation into the performance characteristics of 11 different colour and depth fusion approaches, including 4 fusion and one non fusion approach; using colour and two types of depth images. Trained and tested on over 600 labelled trip hazards over 4 floors and 2000m$\mathrm{^{2}}$ in an active construction site,this approach was able to differentiate between identical objects in different physical configurations (see Figure 1). Outperforming a colour-only detector, our multi-modal trip detector fuses colour and depth information to achieve a 4% absolute improvement in F1-score. These investigative results and the extensive publicly available dataset moves us one step closer to assistive or fully automated safety inspection systems on construction sites.

Multi-Modal Trip Hazard Affordance Detection On Construction Sites

http://arxiv.org/pdf/1706.06718v1

Title MultiModal Trip Hazard Affordance Detection Construction Sites Summary Trip hazard significant contributor accident construction manufacturing site third Australian workplace injury occur 1 Current safety inspection labour intensive limited human fallibilitymaking automation trip hazard detection appealing safety economic perspective Trip hazard present interesting challenge modern learning technique defined much affordance object type example wire table trip hazard lying ground address challenge conduct comprehensive investigation performance characteristic 11 different colour depth fusion approach including 4 fusion one non fusion approach using colour two type depth image Trained tested 600 labelled trip hazard 4 floor 2000mmathrm2 active construction sitethis approach able differentiate identical object different physical configuration see Figure 1 Outperforming colouronly detector multimodal trip detector fuse colour depth information achieve 4 absolute improvement F1score investigative result extensive publicly available dataset move u one step closer assistive fully automated safety inspection system construction site Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Chie Furusawa', 'Kazuyuki Hiroshiba', 'Keisuke Ogaki', 'Yuri Odagiri']

1706.06759v4

We developed "Comicolorization", a semi-automatic colorization system for manga images. Given a monochrome manga and reference images as inputs, our system generates a plausible color version of the manga. This is the first work to address the colorization of an entire manga title (a set of manga pages). Our method colorizes a whole page (not a single panel) semi-automatically, with the same color for the same character across multiple panels. To colorize the target character by the color from the reference image, we extract a color feature from the reference and feed it to the colorization network to help the colorization. Our approach employs adversarial loss to encourage the effect of the color features. Optionally, our tool allows users to revise the colorization result interactively. By feeding the color features to our deep colorization network, we accomplish colorization of the entire manga using the desired colors for each panel.

Comicolorization: Semi-Automatic Manga Colorization

http://arxiv.org/pdf/1706.06759v4

Title Comicolorization SemiAutomatic Manga Colorization Summary developed Comicolorization semiautomatic colorization system manga image Given monochrome manga reference image input system generates plausible color version manga first work address colorization entire manga title set manga page method colorizes whole page single panel semiautomatically color character across multiple panel colorize target character color reference image extract color feature reference feed colorization network help colorization approach employ adversarial loss encourage effect color feature Optionally tool allows user revise colorization result interactively feeding color feature deep colorization network accomplish colorization entire manga using desired color panel Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Paulina Hensman', 'Kiyoharu Aizawa']

1706.06918v1

The Japanese comic format known as Manga is popular all over the world. It is traditionally produced in black and white, and colorization is time consuming and costly. Automatic colorization methods generally rely on greyscale values, which are not present in manga. Furthermore, due to copyright protection, colorized manga available for training is scarce. We propose a manga colorization method based on conditional Generative Adversarial Networks (cGAN). Unlike previous cGAN approaches that use many hundreds or thousands of training images, our method requires only a single colorized reference image for training, avoiding the need of a large dataset. Colorizing manga using cGANs can produce blurry results with artifacts, and the resolution is limited. We therefore also propose a method of segmentation and color-correction to mitigate these issues. The final results are sharp, clear, and in high resolution, and stay true to the character's original color scheme.

cGAN-based Manga Colorization Using a Single Training Image

http://arxiv.org/pdf/1706.06918v1

Title cGANbased Manga Colorization Using Single Training Image Summary Japanese comic format known Manga popular world traditionally produced black white colorization time consuming costly Automatic colorization method generally rely greyscale value present manga Furthermore due copyright protection colorized manga available training scarce propose manga colorization method based conditional Generative Adversarial Networks cGAN Unlike previous cGAN approach use many hundred thousand training image method requires single colorized reference image training avoiding need large dataset Colorizing manga using cGANs produce blurry result artifact resolution limited therefore also propose method segmentation colorcorrection mitigate issue final result sharp clear high resolution stay true character original color scheme Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Brian Guenter', 'Neel Joshi', 'Richard Stoakley', 'Andrew Keefe', 'Kevin Geary', 'Ryan Freeman', 'Jake Hundley', 'Pamela Patterson', 'David Hammon', 'Guillermo Herrera', 'Elena Sherman', 'Andrew Nowak', 'Randall Schubert', 'Peter Brewer', 'Louis Yang', 'Russell Mott', 'Geoff McKnight']

1706.07041v1

The significant optical and size benefits of using a curved focal surface for imaging systems have been well studied yet never brought to market for lack of a high-quality, mass-producible, curved image sensor. In this work we demonstrate that commercial silicon CMOS image sensors can be thinned and formed into accurate, highly curved optical surfaces with undiminished functionality. Our key development is a pneumatic forming process that avoids rigid mechanical constraints and suppresses wrinkling instabilities. A combination of forming-mold design, pressure membrane elastic properties, and controlled friction forces enables us to gradually contact the die at the corners and smoothly press the sensor into a spherical shape. Allowing the die to slide into the concave target shape enables a threefold increase in the spherical curvature over prior approaches having mechanical constraints that resist deformation, and create a high-stress, stretch-dominated state. Our process creates a bridge between the high precision and low-cost but planar CMOS process, and ideal non-planar component shapes such as spherical imagers for improved optical systems. We demonstrate these curved sensors in prototype cameras with custom lenses, measuring exceptional resolution of 3220 line-widths per picture height at an aperture of f/1.2 and nearly 100% relative illumination across the field. Though we use a 1/2.3" format image sensor in this report, we also show this process is generally compatible with many state of the art imaging sensor formats. By example, we report photogrammetry test data for an APS-C sized silicon die formed to a 30$^\circ$ subtended spherical angle. These gains in sharpness and relative illumination enable a new generation of ultra-high performance, manufacturable, digital imaging systems for scientific, industrial, and artistic use.

Highly curved image sensors: a practical approach for improved optical performance

http://arxiv.org/pdf/1706.07041v1

Title Highly curved image sensor practical approach improved optical performance Summary significant optical size benefit using curved focal surface imaging system well studied yet never brought market lack highquality massproducible curved image sensor work demonstrate commercial silicon CMOS image sensor thinned formed accurate highly curved optical surface undiminished functionality key development pneumatic forming process avoids rigid mechanical constraint suppresses wrinkling instability combination formingmold design pressure membrane elastic property controlled friction force enables u gradually contact die corner smoothly press sensor spherical shape Allowing die slide concave target shape enables threefold increase spherical curvature prior approach mechanical constraint resist deformation create highstress stretchdominated state process creates bridge high precision lowcost planar CMOS process ideal nonplanar component shape spherical imagers improved optical system demonstrate curved sensor prototype camera custom lens measuring exceptional resolution 3220 linewidths per picture height aperture f12 nearly 100 relative illumination across field Though use 123 format image sensor report also show process generally compatible many state art imaging sensor format example report photogrammetry test data APSC sized silicon die formed 30circ subtended spherical angle gain sharpness relative illumination enable new generation ultrahigh performance manufacturable digital imaging system scientific industrial artistic use Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Daigo Shoji', 'Rina Noguchi']

1706.07178v1

Shape analyses of tephra grains result in understanding eruption mechanism of volcanoes. However, we have to define and select parameter set such as convexity for the precise discrimination of tephra grains. Selection of the best parameter set for the recognition of tephra shapes is complicated. Actually, many shape parameters have been suggested. Recently, neural network has made a great success in the field of machine learning. Convolutional neural network can recognize the shape of images without human bias and shape parameters. We applied the simple convolutional neural network developed for the handwritten digits to the recognition of tephra shapes. The network was trained by Morphologi tephra images, and it can recognize the tephra shapes with approximately 90% of accuracy.

Shape recognition of volcanic ash by simple convolutional neural network

http://arxiv.org/pdf/1706.07178v1

Title Shape recognition volcanic ash simple convolutional neural network Summary Shape analysis tephra grain result understanding eruption mechanism volcano However define select parameter set convexity precise discrimination tephra grain Selection best parameter set recognition tephra shape complicated Actually many shape parameter suggested Recently neural network made great success field machine learning Convolutional neural network recognize shape image without human bias shape parameter applied simple convolutional neural network developed handwritten digit recognition tephra shape network trained Morphologi tephra image recognize tephra shape approximately 90 accuracy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Athanasios D. Balomenos', 'Elias S. Manolakos']

1706.07359v1

Cell segmentation and tracking allow us to extract a plethora of cell attributes from bacterial time-lapse cell movies, thus promoting computational modeling and simulation of biological processes down to the single-cell level. However, to analyze successfully complex cell movies, imaging multiple interacting bacterial clones as they grow and merge to generate overcrowded bacterial communities with thousands of cells in the field of view, segmentation results should be near perfect to warrant good tracking results. We introduce here a fully automated closed-loop bio-inspired computational strategy that exploits prior knowledge about the expected structure of a colony's lineage tree to locate and correct segmentation errors in analyzed movie frames. We show that this correction strategy is effective, resulting in improved cell tracking and consequently trustworthy deep colony lineage trees. Our image analysis approach has the unique capability to keep tracking cells even after clonal subpopulations merge in the movie. This enables the reconstruction of the complete Forest of Lineage Trees (FLT) representation of evolving multi-clonal bacterial communities. Moreover, the percentage of valid cell trajectories extracted from the image analysis almost doubles after segmentation correction. This plethora of trustworthy data extracted from a complex cell movie analysis enables single-cell analytics as a tool for addressing compelling questions for human health, such as understanding the role of single-cell stochasticity in antibiotics resistance without losing site of the inter-cellular interactions and microenvironment effects that may shape it.

Reconstructing the Forest of Lineage Trees of Diverse Bacterial Communities Using Bio-inspired Image Analysis

http://arxiv.org/pdf/1706.07359v1

Title Reconstructing Forest Lineage Trees Diverse Bacterial Communities Using Bioinspired Image Analysis Summary Cell segmentation tracking allow u extract plethora cell attribute bacterial timelapse cell movie thus promoting computational modeling simulation biological process singlecell level However analyze successfully complex cell movie imaging multiple interacting bacterial clone grow merge generate overcrowded bacterial community thousand cell field view segmentation result near perfect warrant good tracking result introduce fully automated closedloop bioinspired computational strategy exploit prior knowledge expected structure colony lineage tree locate correct segmentation error analyzed movie frame show correction strategy effective resulting improved cell tracking consequently trustworthy deep colony lineage tree image analysis approach unique capability keep tracking cell even clonal subpopulation merge movie enables reconstruction complete Forest Lineage Trees FLT representation evolving multiclonal bacterial community Moreover percentage valid cell trajectory extracted image analysis almost double segmentation correction plethora trustworthy data extracted complex cell movie analysis enables singlecell analytics tool addressing compelling question human health understanding role singlecell stochasticity antibiotic resistance without losing site intercellular interaction microenvironment effect may shape Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sharad Joshi', 'Nitin Khanna']

1706.07422v1

An important aspect of examining printed documents for potential forgeries and copyright infringement is the identification of source printer as it can be helpful for ascertaining the leak and detecting forged documents. This paper proposes a system for classification of source printer from scanned images of printed documents using all the printed letters simultaneously. This system uses local texture patterns based features and a single classifier for classifying all the printed letters. Letters are extracted from scanned images using connected component analysis followed by morphological filtering without the need of using an OCR. Each letter is sub-divided into a flat region and an edge region, and local tetra patterns are estimated separately for these two regions. A strategically constructed pooling technique is used to extract the final feature vectors. The proposed method has been tested on both a publicly available dataset of 10 printers and a new dataset of 18 printers scanned at a resolution of 600 dpi as well as 300 dpi printed in four different fonts. The results indicate shape independence property in the proposed method as using a single classifier it outperforms existing handcrafted feature-based methods and needs much smaller number of training pages by using all the printed letters.

Single Classifier-based Passive System for Source Printer Classification using Local Texture Features

http://arxiv.org/pdf/1706.07422v1

Title Single Classifierbased Passive System Source Printer Classification using Local Texture Features Summary important aspect examining printed document potential forgery copyright infringement identification source printer helpful ascertaining leak detecting forged document paper proposes system classification source printer scanned image printed document using printed letter simultaneously system us local texture pattern based feature single classifier classifying printed letter Letters extracted scanned image using connected component analysis followed morphological filtering without need using OCR letter subdivided flat region edge region local tetra pattern estimated separately two region strategically constructed pooling technique used extract final feature vector proposed method tested publicly available dataset 10 printer new dataset 18 printer scanned resolution 600 dpi well 300 dpi printed four different font result indicate shape independence property proposed method using single classifier outperforms existing handcrafted featurebased method need much smaller number training page using printed letter Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher 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 Ghayoumi', 'Arvind Bansal']

1706.07757v1

This article describes how to deploy dihedral group theory to detect Facial Key Points (FKP) symmetry to recognize emotions. The method can be applied in many other areas which those have the same data texture.

Improved Human Emotion Recognition Using Symmetry of Facial Key Points with Dihedral Group

http://arxiv.org/pdf/1706.07757v1

Title Improved Human Emotion Recognition Using Symmetry Facial Key Points Dihedral Group Summary article describes deploy dihedral group theory detect Facial Key Points FKP symmetry recognize emotion method applied many area data texture Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yaqi Liu', 'Qingxiao Guan', 'Xianfeng Zhao', 'Yun Cao']

1706.07842v4

In this paper, we propose to utilize Convolutional Neural Networks (CNNs) and the segmentation-based multi-scale analysis to locate tampered areas in digital images. First, to deal with color input sliding windows of different scales, a unified CNN architecture is designed. Then, we elaborately design the training procedures of CNNs on sampled training patches. With a set of robust multi-scale tampering detectors based on CNNs, complementary tampering possibility maps can be generated. Last but not least, a segmentation-based method is proposed to fuse the maps and generate the final decision map. By exploiting the benefits of both the small-scale and large-scale analyses, the segmentation-based multi-scale analysis can lead to a performance leap in forgery localization of CNNs. Numerous experiments are conducted to demonstrate the effectiveness and efficiency of our method.

Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks

http://arxiv.org/pdf/1706.07842v4

Title Image Forgery Localization Based MultiScale Convolutional Neural Networks Summary paper propose utilize Convolutional Neural Networks CNNs segmentationbased multiscale analysis locate tampered area digital image First deal color input sliding window different scale unified CNN architecture designed elaborately design training procedure CNNs sampled training patch set robust multiscale tampering detector based CNNs complementary tampering possibility map generated Last least segmentationbased method proposed fuse map generate final decision map exploiting benefit smallscale largescale analysis segmentationbased multiscale analysis lead performance leap forgery localization CNNs Numerous experiment conducted demonstrate effectiveness efficiency 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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['Jian Zhang', 'Bernard Ghanem']

1706.07929v1

Traditional methods for image compressive sensing (CS) reconstruction solve a well-defined inverse problem that is based on a predefined CS model, which defines the underlying structure of the problem and is generally solved by employing convergent iterative solvers. These optimization-based CS methods face the challenge of choosing optimal transforms and tuning parameters in their solvers, while also suffering from high computational complexity in most cases. Recently, some deep network based CS algorithms have been proposed to improve CS reconstruction performance, while dramatically reducing time complexity as compared to optimization-based methods. Despite their impressive results, the proposed networks (either with fully-connected or repetitive convolutional layers) lack any structural diversity and they are trained as a black box, void of any insights from the CS domain. In this paper, we combine the merits of both types of CS methods: the structure insights of optimization-based method and the performance/speed of network-based ones. We propose a novel structured deep network, dubbed ISTA-Net, which is inspired by the Iterative Shrinkage-Thresholding Algorithm (ISTA) for optimizing a general $l_1$ norm CS reconstruction model. ISTA-Net essentially implements a truncated form of ISTA, where all ISTA-Net parameters are learned end-to-end to minimize a reconstruction error in training. Borrowing more insights from the optimization realm, we propose an accelerated version of ISTA-Net, dubbed FISTA-Net, which is inspired by the fast iterative shrinkage-thresholding algorithm (FISTA). Interestingly, this acceleration naturally leads to skip connections in the underlying network design. Extensive CS experiments demonstrate that the proposed ISTA-Net and FISTA-Net outperform existing optimization-based and network-based CS methods by large margins, while maintaining a fast runtime.

ISTA-Net: Iterative Shrinkage-Thresholding Algorithm Inspired Deep Network for Image Compressive Sensing

http://arxiv.org/pdf/1706.07929v1

Title ISTANet Iterative ShrinkageThresholding Algorithm Inspired Deep Network Image Compressive Sensing Summary Traditional method image compressive sensing CS reconstruction solve welldefined inverse problem based predefined CS model defines underlying structure problem generally solved employing convergent iterative solver optimizationbased CS method face challenge choosing optimal transforms tuning parameter solver also suffering high computational complexity case Recently deep network based CS algorithm proposed improve CS reconstruction performance dramatically reducing time complexity compared optimizationbased method Despite impressive result proposed network either fullyconnected repetitive convolutional layer lack structural diversity trained black box void insight CS domain paper combine merit type CS method structure insight optimizationbased method performancespeed networkbased one propose novel structured deep network dubbed ISTANet inspired Iterative ShrinkageThresholding Algorithm ISTA optimizing general l1 norm CS reconstruction model ISTANet essentially implement truncated form ISTA ISTANet parameter learned endtoend minimize reconstruction error training Borrowing insight optimization realm propose accelerated version ISTANet dubbed FISTANet inspired fast iterative shrinkagethresholding algorithm FISTA Interestingly acceleration naturally lead skip connection underlying network design Extensive CS experiment demonstrate proposed ISTANet FISTANet outperform existing optimizationbased networkbased CS method large margin maintaining fast runtime Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Adrien Coppens']

1706.08096v1

Achieving a symbiotic blending between reality and virtuality is a dream that has been lying in the minds of many people for a long time. Advances in various domains constantly bring us closer to making that dream come true. Augmented reality as well as virtual reality are in fact trending terms and are expected to further progress in the years to come. This master's thesis aims to explore these areas and starts by defining necessary terms such as augmented reality (AR) or virtual reality (VR). Usual taxonomies to classify and compare the corresponding experiences are then discussed. In order to enable those applications, many technical challenges need to be tackled, such as accurate motion tracking with 6 degrees of freedom (positional and rotational), that is necessary for compelling experiences and to prevent user sickness. Additionally, augmented reality experiences typically rely on image processing to position the superimposed content. To do so, "paper" markers or features extracted from the environment are often employed. Both sets of techniques are explored and common solutions and algorithms are presented. After investigating those technical aspects, I carry out an objective comparison of the existing state-of-the-art and state-of-the-practice in those domains, and I discuss present and potential applications in these areas. As a practical validation, I present the results of an application that I have developed using Microsoft HoloLens, one of the more advanced affordable technologies for augmented reality that is available today. Based on the experience and lessons learned during this development, I discuss the limitations of current technologies and present some avenues of future research.

Merging real and virtual worlds: An analysis of the state of the art and practical evaluation of Microsoft Hololens

http://arxiv.org/pdf/1706.08096v1

Title Merging real virtual world analysis state art practical evaluation Microsoft Hololens Summary Achieving symbiotic blending reality virtuality dream lying mind many people long time Advances various domain constantly bring u closer making dream come true Augmented reality well virtual reality fact trending term expected progress year come master thesis aim explore area start defining necessary term augmented reality AR virtual reality VR Usual taxonomy classify compare corresponding experience discussed order enable application many technical challenge need tackled accurate motion tracking 6 degree freedom positional rotational necessary compelling experience prevent user sickness Additionally augmented reality experience typically rely image processing position superimposed content paper marker feature extracted environment often employed set technique explored common solution algorithm presented investigating technical aspect carry objective comparison existing stateoftheart stateofthepractice domain discus present potential application area practical validation present result application developed using Microsoft HoloLens one advanced affordable technology augmented reality available today Based experience lesson learned development discus limitation current technology present avenue future research Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ayush Dewan', 'Gabriel L. Oliveira', 'Wolfram Burgard']

1706.08355v1

Robots are expected to operate autonomously in dynamic environments. Understanding the underlying dynamic characteristics of objects is a key enabler for achieving this goal. In this paper, we propose a method for pointwise semantic classification of 3D LiDAR data into three classes: non-movable, movable and dynamic. We concentrate on understanding these specific semantics because they characterize important information required for an autonomous system. Non-movable points in the scene belong to unchanging segments of the environment, whereas the remaining classes corresponds to the changing parts of the scene. The difference between the movable and dynamic class is their motion state. The dynamic points can be perceived as moving, whereas movable objects can move, but are perceived as static. To learn the distinction between movable and non-movable points in the environment, we introduce an approach based on deep neural network and for detecting the dynamic points, we estimate pointwise motion. We propose a Bayes filter framework for combining the learned semantic cues with the motion cues to infer the required semantic classification. In extensive experiments, we compare our approach with other methods on a standard benchmark dataset and report competitive results in comparison to the existing state-of-the-art. Furthermore, we show an improvement in the classification of points by combining the semantic cues retrieved from the neural network with the motion cues.

Deep Semantic Classification for 3D LiDAR Data

http://arxiv.org/pdf/1706.08355v1

Title Deep Semantic Classification 3D LiDAR Data Summary Robots expected operate autonomously dynamic environment Understanding underlying dynamic characteristic object key enabler achieving goal paper propose method pointwise semantic classification 3D LiDAR data three class nonmovable movable dynamic concentrate understanding specific semantics characterize important information required autonomous system Nonmovable point scene belong unchanging segment environment whereas remaining class corresponds changing part scene difference movable dynamic class motion state dynamic point perceived moving whereas movable object move perceived static learn distinction movable nonmovable point environment introduce approach based deep neural network detecting dynamic point estimate pointwise motion propose Bayes filter framework combining learned semantic cue motion cue infer required semantic classification extensive experiment compare approach method standard benchmark dataset report competitive result comparison existing stateoftheart Furthermore show improvement classification point combining semantic cue retrieved neural network motion cue Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Juan Garcia-Torres', 'Diana Caro-Prieto']

1706.08436v1

Colombia has a privileged geographical location which makes it a cornerstone and equidistant point to all regional markets. The country has a great ecological diversity and it is one of the largest suppliers of flowers for US. Colombian flower companies have made innovations in the marketing process, using methods to reach all conditions for final consumers. This article develops a monitoring system for floriculture industries. The system was implemented in a robotic platform. This device has the ability to be programmed in different programming languages. The robot takes the necessary environment information from its camera. The algorithm of the monitoring system was developed with the image processing toolbox on Matlab. The implemented algorithm acquires images through its camera, it performs a preprocessing of the image, noise filter, enhancing of the color and adjusting the dimension in order to increase processing speed. Then, the image is segmented by color and with the binarized version of the image using morphological operations (erosion and dilation), extract relevant features such as centroid, perimeter and area. The data obtained from the image processing helps the robot with the automatic identification of objectives, orientation and move towards them. Also, the results generate a diagnostic quality of each object scanned.

Image Processing in Floriculture Using a robotic Mobile Platform

http://arxiv.org/pdf/1706.08436v1

Title Image Processing Floriculture Using robotic Mobile Platform Summary Colombia privileged geographical location make cornerstone equidistant point regional market country great ecological diversity one largest supplier flower US Colombian flower company made innovation marketing process using method reach condition final consumer article develops monitoring system floriculture industry system implemented robotic platform device ability programmed different programming language robot take necessary environment information camera algorithm monitoring system developed image processing toolbox Matlab implemented algorithm acquires image camera performs preprocessing image noise filter enhancing color adjusting dimension order increase processing speed image segmented color binarized version image using morphological operation erosion dilation extract relevant feature centroid perimeter area data obtained image processing help robot automatic identification objective orientation move towards Also result generate diagnostic quality object scanned Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher 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 McKay', 'Anne Gelb', 'Vishal Monga', 'Raghu Raj']

1706.08575v1

Recent progress in synthetic aperture sonar (SAS) technology and processing has led to significant advances in underwater imaging, outperforming previously common approaches in both accuracy and efficiency. There are, however, inherent limitations to current SAS reconstruction methodology. In particular, popular and efficient Fourier domain SAS methods require a 2D interpolation which is often ill conditioned and inaccurate, inevitably reducing robustness with regard to speckle and inaccurate sound-speed estimation. To overcome these issues, we propose using the frame theoretic convolution gridding (FTCG) algorithm to handle the non-uniform Fourier data. FTCG extends upon non-uniform fast Fourier transform (NUFFT) algorithms by casting the NUFFT as an approximation problem given Fourier frame data. The FTCG has been show to yield improved accuracy at little more computational cost. Using simulated data, we outline how the FTCG can be used to enhance current SAS processing.

Using Frame Theoretic Convolutional Gridding for Robust Synthetic Aperture Sonar Imaging

http://arxiv.org/pdf/1706.08575v1

Title Using Frame Theoretic Convolutional Gridding Robust Synthetic Aperture Sonar Imaging Summary Recent progress synthetic aperture sonar SAS technology processing led significant advance underwater imaging outperforming previously common approach accuracy efficiency however inherent limitation current SAS reconstruction methodology particular popular efficient Fourier domain SAS method require 2D interpolation often ill conditioned inaccurate inevitably reducing robustness regard speckle inaccurate soundspeed estimation overcome issue propose using frame theoretic convolution gridding FTCG algorithm handle nonuniform Fourier data FTCG extends upon nonuniform fast Fourier transform NUFFT algorithm casting NUFFT approximation problem given Fourier frame data FTCG show yield improved accuracy little computational cost Using simulated data outline FTCG used enhance current SAS processing Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Martim Brandao', 'Yukitoshi Minami Shiguematsu', 'Kenji Hashimoto', 'Atsuo Takanishi']

1706.08685v1

In this paper we tackle the problem of visually predicting surface friction for environments with diverse surfaces, and integrating this knowledge into biped robot locomotion planning. The problem is essential for autonomous robot locomotion since diverse surfaces with varying friction abound in the real world, from wood to ceramic tiles, grass or ice, which may cause difficulties or huge energy costs for robot locomotion if not considered. We propose to estimate friction and its uncertainty from visual estimation of material classes using convolutional neural networks, together with probability distribution functions of friction associated with each material. We then robustly integrate the friction predictions into a hierarchical (footstep and full-body) planning method using chance constraints, and optimize the same trajectory costs at both levels of the planning method for consistency. Our solution achieves fully autonomous perception and locomotion on slippery terrain, which considers not only friction and its uncertainty, but also collision, stability and trajectory cost. We show promising friction prediction results in real pictures of outdoor scenarios, and planning experiments on a real robot facing surfaces with different friction.

Material Recognition CNNs and Hierarchical Planning for Biped Robot Locomotion on Slippery Terrain

http://arxiv.org/pdf/1706.08685v1

Title Material Recognition CNNs Hierarchical Planning Biped Robot Locomotion Slippery Terrain Summary paper tackle problem visually predicting surface friction environment diverse surface integrating knowledge biped robot locomotion planning problem essential autonomous robot locomotion since diverse surface varying friction abound real world wood ceramic tile grass ice may cause difficulty huge energy cost robot locomotion considered propose estimate friction uncertainty visual estimation material class using convolutional neural network together probability distribution function friction associated material robustly integrate friction prediction hierarchical footstep fullbody planning method using chance constraint optimize trajectory cost level planning method consistency solution achieves fully autonomous perception locomotion slippery terrain considers friction uncertainty also collision stability trajectory cost show promising friction prediction result real picture outdoor scenario planning experiment real robot facing surface different friction Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Gabriel L. Oliveira', 'Noha Radwan', 'Wolfram Burgard', 'Thomas Brox']

1706.08775v1

Compared to LiDAR-based localization methods, which provide high accuracy but rely on expensive sensors, visual localization approaches only require a camera and thus are more cost-effective while their accuracy and reliability typically is inferior to LiDAR-based methods. In this work, we propose a vision-based localization approach that learns from LiDAR-based localization methods by using their output as training data, thus combining a cheap, passive sensor with an accuracy that is on-par with LiDAR-based localization. The approach consists of two deep networks trained on visual odometry and topological localization, respectively, and a successive optimization to combine the predictions of these two networks. We evaluate the approach on a new challenging pedestrian-based dataset captured over the course of six months in varying weather conditions with a high degree of noise. The experiments demonstrate that the localization errors are up to 10 times smaller than with traditional vision-based localization methods.

Topometric Localization with Deep Learning

http://arxiv.org/pdf/1706.08775v1

Title Topometric Localization Deep Learning Summary Compared LiDARbased localization method provide high accuracy rely expensive sensor visual localization approach require camera thus costeffective accuracy reliability typically inferior LiDARbased method work propose visionbased localization approach learns LiDARbased localization method using output training data thus combining cheap passive sensor accuracy onpar LiDARbased localization approach consists two deep network trained visual odometry topological localization respectively successive optimization combine prediction two network evaluate approach new challenging pedestrianbased dataset captured course six month varying weather condition high degree noise experiment demonstrate localization error 10 time smaller traditional visionbased localization 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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['Jorge Calvo-Zaragoza', 'Antonio-Javier Gallego']

1706.10241v2

Binarization plays a key role in the automatic information retrieval from document images. This process is usually performed in the first stages of documents analysis systems, and serves as a basis for subsequent steps. Hence it has to be robust in order to allow the full analysis workflow to be successful. Several methods for document image binarization have been proposed so far, most of which are based on hand-crafted image processing strategies. Recently, Convolutional Neural Networks have shown an amazing performance in many disparate duties related to computer vision. In this paper we discuss the use of convolutional auto-encoders devoted to learning an end-to-end map from an input image to its selectional output, in which activations indicate the likelihood of pixels to be either foreground or background. Once trained, documents can therefore be binarized by parsing them through the model and applying a threshold. This approach has proven to outperform existing binarization strategies in a number of document domains.

A selectional auto-encoder approach for document image binarization

http://arxiv.org/pdf/1706.10241v2

Title selectional autoencoder approach document image binarization Summary Binarization play key role automatic information retrieval document image process usually performed first stage document analysis system serf basis subsequent step Hence robust order allow full analysis workflow successful Several method document image binarization proposed far based handcrafted image processing strategy Recently Convolutional Neural Networks shown amazing performance many disparate duty related computer vision paper discus use convolutional autoencoders devoted learning endtoend map input image selectional output activation indicate likelihood pixel either foreground background trained document therefore binarized parsing model applying threshold approach proven outperform existing binarization strategy number document domain Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Manikandasriram Srinivasan Ramanagopal', 'Cyrus Anderson', 'Ram Vasudevan', 'Matthew Johnson-Roberson']

1707.00051v2

One of the major open challenges in self-driving cars is the ability to detect cars and pedestrians to safely navigate in the world. Deep learning-based object detector approaches have enabled great advances in using camera imagery to detect and classify objects. But for a safety critical application such as autonomous driving, the error rates of the current state-of-the-art are still too high to enable safe operation. Moreover, our characterization of object detector performance is primarily limited to testing on prerecorded datasets. Errors that occur on novel data go undetected without additional human labels. In this paper, we propose an automated method to identify mistakes made by object detectors without ground truth labels. We show that inconsistencies in object detector output between a pair of similar images can be used to identify false negatives(e.g. missed detections). In particular, we study two distinct cues - temporal and stereo inconsistencies - using data that is readily available on most autonomous vehicles. Our method can be used with any camera-based object detector and we evaluate the technique on several sets of real world data. The proposed method achieves over 97% precision in automatically identifying missed detections produced by one of the leading state-of-the-art object detectors in the literature. We also release a new tracking dataset with over 100 sequences totaling more than 80,000 labeled images from a game engine to facilitate further research.

Failing to Learn: Autonomously Identifying Perception Failures for Self-driving Cars

http://arxiv.org/pdf/1707.00051v2

Title Failing Learn Autonomously Identifying Perception Failures Selfdriving Cars Summary One major open challenge selfdriving car ability detect car pedestrian safely navigate world Deep learningbased object detector approach enabled great advance using camera imagery detect classify object safety critical application autonomous driving error rate current stateoftheart still high enable safe operation Moreover characterization object detector performance primarily limited testing prerecorded datasets Errors occur novel data go undetected without additional human label paper propose automated method identify mistake made object detector without ground truth label show inconsistency object detector output pair similar image used identify false negativeseg missed detection particular study two distinct cue temporal stereo inconsistency using data readily available autonomous vehicle method used camerabased object detector evaluate technique several set real world data proposed method achieves 97 precision automatically identifying missed detection produced one leading stateoftheart object detector literature also release new tracking dataset 100 sequence totaling 80000 labeled image game engine facilitate research Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Stepan Tulyakov', 'Anton Ivanov', 'Nicolas Thomas', 'Victoria Roloff', 'Antoine Pommerol', 'Gabriele Cremonese', 'Thomas Weigel', 'Francois Fleuret']

1707.00606v1

There are many geometric calibration methods for "standard" cameras. These methods, however, cannot be used for the calibration of telescopes with large focal lengths and complex off-axis optics. Moreover, specialized calibration methods for the telescopes are scarce in literature. We describe the calibration method that we developed for the Colour and Stereo Surface Imaging System (CaSSIS) telescope, on board of the ExoMars Trace Gas Orbiter (TGO). Although our method is described in the context of CaSSIS, with camera-specific experiments, it is general and can be applied to other telescopes. We further encourage re-use of the proposed method by making our calibration code and data available on-line.

Geometric calibration of Colour and Stereo Surface Imaging System of ESA's Trace Gas Orbiter

http://arxiv.org/pdf/1707.00606v1

Title Geometric calibration Colour Stereo Surface Imaging System ESAs Trace Gas Orbiter Summary many geometric calibration method standard camera method however cannot used calibration telescope large focal length complex offaxis optic Moreover specialized calibration method telescope scarce literature describe calibration method developed Colour Stereo Surface Imaging System CaSSIS telescope board ExoMars Trace Gas Orbiter TGO Although method described context CaSSIS cameraspecific experiment general applied telescope encourage reuse proposed method making calibration code data available online Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mahmoud A. A. Mousa', 'Mohammed S. Sayed', 'Mahmoud I. Abdalla']

1707.00800v1

Arabic is one of the languages that present special challenges to Optical character recognition (OCR). The main challenge in Arabic is that it is mostly cursive. Therefore, a segmentation process must be carried out to determine where the character begins and where it ends. This step is essential for character recognition. This paper presents Arabic character segmentation algorithm. The proposed algorithm uses the projection-based approach concepts to separate lines, words, and characters. This is done using profile's amplitude filter and simple edge tool to find characters separations. Our algorithm shows promising performance when applied on different machine printed documents with different Arabic fonts.

Arabic Character Segmentation Using Projection Based Approach with Profile's Amplitude Filter

http://arxiv.org/pdf/1707.00800v1

Title Arabic Character Segmentation Using Projection Based Approach Profiles Amplitude Filter Summary Arabic one language present special challenge Optical character recognition OCR main challenge Arabic mostly cursive Therefore segmentation process must carried determine character begin end step essential character recognition paper present Arabic character segmentation algorithm proposed algorithm us projectionbased approach concept separate line word character done using profile amplitude filter simple edge tool find character separation algorithm show promising performance applied different machine printed document different Arabic font Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yaqi Liu', 'Qingxiao Guan', 'Xianfeng Zhao']

1707.01221v1

In this paper, a copy-move forgery detection method based on Convolutional Kernel Network is proposed. Different from methods based on conventional hand-crafted features, Convolutional Kernel Network is a kind of data-driven local descriptor with the deep convolutional structure. Thanks to the development of deep learning theories and widely available datasets, the data-driven methods can achieve competitive performance on different conditions for its excellent discriminative capability. Besides, our Convolutional Kernel Network is reformulated as a series of matrix computations and convolutional operations which are easy to parallelize and accelerate by GPU, leading to high efficiency. Then, appropriate preprocessing and postprocessing for Convolutional Kernel Network are adopted to achieve copy-move forgery detection. Particularly, a segmentation-based keypoints distribution strategy is proposed and a GPU-based adaptive oversegmentation method is adopted. Numerous experiments are conducted to demonstrate the effectiveness and robustness of the GPU version of Convolutional Kernel Network, and the state-of-the-art performance of the proposed copy-move forgery detection method based on Convolutional Kernel Network.

Copy-move Forgery Detection based on Convolutional Kernel Network

http://arxiv.org/pdf/1707.01221v1

Title Copymove Forgery Detection based Convolutional Kernel Network Summary paper copymove forgery detection method based Convolutional Kernel Network proposed Different method based conventional handcrafted feature Convolutional Kernel Network kind datadriven local descriptor deep convolutional structure Thanks development deep learning theory widely available datasets datadriven method achieve competitive performance different condition excellent discriminative capability Besides Convolutional Kernel Network reformulated series matrix computation convolutional operation easy parallelize accelerate GPU leading high efficiency appropriate preprocessing postprocessing Convolutional Kernel Network adopted achieve copymove forgery detection Particularly segmentationbased keypoints distribution strategy proposed GPUbased adaptive oversegmentation method adopted Numerous experiment conducted demonstrate effectiveness robustness GPU version Convolutional Kernel Network stateoftheart performance proposed copymove forgery detection method based Convolutional Kernel Network Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hamideh Rezaee', 'Brian Tracey', 'Eric L. Miller']

1707.01530v1

In this paper we demonstrate the utility of fusing energy-resolved observations of Compton scattered photons with traditional attenuation data for the joint recovery of mass density and photoelectric absorption in the context of limited view tomographic imaging applications. We begin with the development of a physical and associated numerical model for the Compton scatter process. Using this model, we propose a variational approach recovering these two material properties. In addition to the typical data-fidelity terms, the optimization functional includes regularization for both the mass density and photoelectric coefficients. We consider a novel edge-preserving method in the case of mass density. To aid in the recovery of the photoelectric information, we draw on our recent method in \cite{r15} and employ a non-local regularization scheme that builds on the fact that mass density is more stably imaged. Simulation results demonstrate clear advantages associated with the use of both scattered photon data and energy resolved information in mapping the two material properties of interest. Specifically, comparing images obtained using only conventional attenuation data with those where we employ only Compton scatter photons and images formed from the combination of the two, shows that taking advantage of both types of data for reconstruction provides far more accurate results.

On the Fusion of Compton Scatter and Attenuation Data for Limited-view X-ray Tomographic Applications

http://arxiv.org/pdf/1707.01530v1

Title Fusion Compton Scatter Attenuation Data Limitedview Xray Tomographic Applications Summary paper demonstrate utility fusing energyresolved observation Compton scattered photon traditional attenuation data joint recovery mass density photoelectric absorption context limited view tomographic imaging application begin development physical associated numerical model Compton scatter process Using model propose variational approach recovering two material property addition typical datafidelity term optimization functional includes regularization mass density photoelectric coefficient consider novel edgepreserving method case mass density aid recovery photoelectric information draw recent method citer15 employ nonlocal regularization scheme build fact mass density stably imaged Simulation result demonstrate clear advantage associated use scattered photon data energy resolved information mapping two material property interest Specifically comparing image obtained using conventional attenuation data employ Compton scatter photon image formed combination two show taking advantage type data reconstruction provides far accurate 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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['Haichao Shi', 'Jing Dong', 'Wei Wang', 'Yinlong Qian', 'Xiaoyu Zhang']

1707.01613v3

In this paper, a novel strategy of Secure Steganograpy based on Generative Adversarial Networks is proposed to generate suitable and secure covers for steganography. The proposed architecture has one generative network, and two discriminative networks. The generative network mainly evaluates the visual quality of the generated images for steganography, and the discriminative networks are utilized to assess their suitableness for information hiding. Different from the existing work which adopts Deep Convolutional Generative Adversarial Networks, we utilize another form of generative adversarial networks. By using this new form of generative adversarial networks, significant improvements are made on the convergence speed, the training stability and the image quality. Furthermore, a sophisticated steganalysis network is reconstructed for the discriminative network, and the network can better evaluate the performance of the generated images. Numerous experiments are conducted on the publicly available datasets to demonstrate the effectiveness and robustness of the proposed method.

SSGAN: Secure Steganography Based on Generative Adversarial Networks

http://arxiv.org/pdf/1707.01613v3

Title SSGAN Secure Steganography Based Generative Adversarial Networks Summary paper novel strategy Secure Steganograpy based Generative Adversarial Networks proposed generate suitable secure cover steganography proposed architecture one generative network two discriminative network generative network mainly evaluates visual quality generated image steganography discriminative network utilized ass suitableness information hiding Different existing work adopts Deep Convolutional Generative Adversarial Networks utilize another form generative adversarial network using new form generative adversarial network significant improvement made convergence speed training stability image quality Furthermore sophisticated steganalysis network reconstructed discriminative network network better evaluate performance generated image Numerous experiment conducted publicly available datasets demonstrate effectiveness robustness 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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['Aritra Dutta', 'Xin Li']

1707.01753v1

Classical principal component analysis (PCA) is not robust to the presence of sparse outliers in the data. The use of the $\ell_1$ norm in the Robust PCA (RPCA) method successfully eliminates the weakness of PCA in separating the sparse outliers. In this paper, by sticking a simple weight to the Frobenius norm, we propose a weighted low rank (WLR) method to avoid the often computationally expensive algorithms relying on the $\ell_1$ norm. As a proof of concept, a background estimation model has been presented and compared with two $\ell_1$ norm minimization algorithms. We illustrate that as long as a simple weight matrix is inferred from the data, one can use the weighted Frobenius norm and achieve the same or better performance.

Weighted Low Rank Approximation for Background Estimation Problems

http://arxiv.org/pdf/1707.01753v1

Title Weighted Low Rank Approximation Background Estimation Problems Summary Classical principal component analysis PCA robust presence sparse outlier data use ell1 norm Robust PCA RPCA method successfully eliminates weakness PCA separating sparse outlier paper sticking simple weight Frobenius norm propose weighted low rank WLR method avoid often computationally expensive algorithm relying ell1 norm proof concept background estimation model presented compared two ell1 norm minimization algorithm illustrate long simple weight matrix inferred data one use weighted Frobenius norm achieve better 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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['Kathlén Kohn', 'Bernd Sturmfels', 'Matthew Trager']

1707.01877v2

Visual events in computer vision are studied from the perspective of algebraic geometry. Given a sufficiently general curve or surface in 3-space, we consider the image or contour curve that arises by projecting from a viewpoint. Qualitative changes in that curve occur when the viewpoint crosses the visual event surface. We examine the components of this ruled surface, and observe that these coincide with the iterated singular loci of the coisotropic hypersurfaces associated with the original curve or surface. We derive formulas, due to Salmon and Petitjean, for the degrees of these surfaces, and show how to compute exact representations for all visual event surfaces using algebraic methods.

Changing Views on Curves and Surfaces

http://arxiv.org/pdf/1707.01877v2

Title Changing Views Curves Surfaces Summary Visual event computer vision studied perspective algebraic geometry Given sufficiently general curve surface 3space consider image contour curve arises projecting viewpoint Qualitative change curve occur viewpoint cross visual event surface examine component ruled surface observe coincide iterated singular locus coisotropic hypersurfaces associated original curve surface derive formula due Salmon Petitjean degree surface show compute exact representation visual event surface using algebraic 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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['Michaël Gharbi', 'Jiawen Chen', 'Jonathan T. Barron', 'Samuel W. Hasinoff', 'Frédo Durand']

1707.02880v2

Performance is a critical challenge in mobile image processing. Given a reference imaging pipeline, or even human-adjusted pairs of images, we seek to reproduce the enhancements and enable real-time evaluation. For this, we introduce a new neural network architecture inspired by bilateral grid processing and local affine color transforms. Using pairs of input/output images, we train a convolutional neural network to predict the coefficients of a locally-affine model in bilateral space. Our architecture learns to make local, global, and content-dependent decisions to approximate the desired image transformation. At runtime, the neural network consumes a low-resolution version of the input image, produces a set of affine transformations in bilateral space, upsamples those transformations in an edge-preserving fashion using a new slicing node, and then applies those upsampled transformations to the full-resolution image. Our algorithm processes high-resolution images on a smartphone in milliseconds, provides a real-time viewfinder at 1080p resolution, and matches the quality of state-of-the-art approximation techniques on a large class of image operators. Unlike previous work, our model is trained off-line from data and therefore does not require access to the original operator at runtime. This allows our model to learn complex, scene-dependent transformations for which no reference implementation is available, such as the photographic edits of a human retoucher.

Deep Bilateral Learning for Real-Time Image Enhancement

http://arxiv.org/pdf/1707.02880v2

Title Deep Bilateral Learning RealTime Image Enhancement Summary Performance critical challenge mobile image processing Given reference imaging pipeline even humanadjusted pair image seek reproduce enhancement enable realtime evaluation introduce new neural network architecture inspired bilateral grid processing local affine color transforms Using pair inputoutput image train convolutional neural network predict coefficient locallyaffine model bilateral space architecture learns make local global contentdependent decision approximate desired image transformation runtime neural network consumes lowresolution version input image produce set affine transformation bilateral space upsamples transformation edgepreserving fashion using new slicing node applies upsampled transformation fullresolution image algorithm process highresolution image smartphone millisecond provides realtime viewfinder 1080p resolution match quality stateoftheart approximation technique large class image operator Unlike previous work model trained offline data therefore require access original operator runtime allows model learn complex scenedependent transformation reference implementation available photographic edits human retoucher Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher 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 Du', 'Yuan Du', 'Yilei Li', 'Mau-Chung Frank Chang']

1707.02973v1

Convolutional neural network (CNN) offers significant accuracy in image detection. To implement image detection using CNN in the internet of things (IoT) devices, a streaming hardware accelerator is proposed. The proposed accelerator optimizes the energy efficiency by avoiding unnecessary data movement. With unique filter decomposition technique, the accelerator can support arbitrary convolution window size. In addition, max pooling function can be computed in parallel with convolution by using separate pooling unit, thus achieving throughput improvement. A prototype accelerator was implemented in TSMC 65nm technology with a core size of 5mm2. The accelerator can support major CNNs and achieve 152GOPS peak throughput and 434GOPS/W energy efficiency at 350mW, making it a promising hardware accelerator for intelligent IoT devices.

A Reconfigurable Streaming Deep Convolutional Neural Network Accelerator for Internet of Things

http://arxiv.org/pdf/1707.02973v1

Title Reconfigurable Streaming Deep Convolutional Neural Network Accelerator Internet Things Summary Convolutional neural network CNN offer significant accuracy image detection implement image detection using CNN internet thing IoT device streaming hardware accelerator proposed proposed accelerator optimizes energy efficiency avoiding unnecessary data movement unique filter decomposition technique accelerator support arbitrary convolution window size addition max pooling function computed parallel convolution using separate pooling unit thus achieving throughput improvement prototype accelerator implemented TSMC 65nm technology core size 5mm2 accelerator support major CNNs achieve 152GOPS peak throughput 434GOPSW energy efficiency 350mW making promising hardware accelerator intelligent IoT device Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Joris Guérin', 'Olivier Gibaru', 'Eric Nyiri', 'Stéphane Thiery']

1707.02978v2

Unlike classification, position labels cannot be assigned manually by humans. For this reason, generating supervision for precise object localization is a hard task. This paper details a method to create large datasets for 3D object localization, with real world images, using an industrial robot to generate position labels. By knowledge of the geometry of the robot, we are able to automatically synchronize the images of the two cameras and the object 3D position. We applied it to generate a screw-driver localization dataset with stereo images, using a KUKA LBR iiwa robot. This dataset could then be used to train a CNN regressor to learn end-to-end stereo object localization from a set of two standard uncalibrated cameras.

Automatic Construction of Real-World Datasets for 3D Object Localization using Two Cameras

http://arxiv.org/pdf/1707.02978v2

Title Automatic Construction RealWorld Datasets 3D Object Localization using Two Cameras Summary Unlike classification position label cannot assigned manually human reason generating supervision precise object localization hard task paper detail method create large datasets 3D object localization real world image using industrial robot generate position label knowledge geometry robot able automatically synchronize image two camera object 3D position applied generate screwdriver localization dataset stereo image using KUKA LBR iiwa robot dataset could used train CNN regressor learn endtoend stereo object localization set two standard uncalibrated camera Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Marc Assens', 'Kevin McGuinness', 'Xavier Giro-i-Nieto', "Noel E. O'Connor"]

1707.03123v5

We introduce SaltiNet, a deep neural network for scanpath prediction trained on 360-degree images. The model is based on a temporal-aware novel representation of saliency information named the saliency volume. The first part of the network consists of a model trained to generate saliency volumes, whose parameters are fit by back-propagation computed from a binary cross entropy (BCE) loss over downsampled versions of the saliency volumes. Sampling strategies over these volumes are used to generate scanpaths over the 360-degree images. Our experiments show the advantages of using saliency volumes, and how they can be used for related tasks. Our source code and trained models available at https://github.com/massens/saliency-360salient-2017.

SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumes

http://arxiv.org/pdf/1707.03123v5

Title SaltiNet Scanpath Prediction 360 Degree Images using Saliency Volumes Summary introduce SaltiNet deep neural network scanpath prediction trained 360degree image model based temporalaware novel representation saliency information named saliency volume first part network consists model trained generate saliency volume whose parameter fit backpropagation computed binary cross entropy BCE loss downsampled version saliency volume Sampling strategy volume used generate scanpaths 360degree image experiment show advantage using saliency volume used related task source code trained model available httpsgithubcommassenssaliency360salient2017 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Liheng Bian', 'Jinli Suo', 'Qionghai Dai', 'Feng Chen']

1707.03164v2

Single-pixel imaging (SPI) is a novel technique capturing 2D images using a photodiode, instead of conventional 2D array sensors. SPI owns high signal-to-noise ratio, wide spectrum range, low cost, and robustness to light scattering. Various algorithms have been proposed for SPI reconstruction, including the linear correlation methods, the alternating projection method (AP), and the compressive sensing based methods. However, there has been no comprehensive review discussing respective advantages, which is important for SPI's further applications and development. In this paper, we reviewed and compared these algorithms in a unified reconstruction framework. Besides, we proposed two other SPI algorithms including a conjugate gradient descent based method (CGD) and a Poisson maximum likelihood based method. Both simulations and experiments validate the following conclusions: to obtain comparable reconstruction accuracy, the compressive sensing based total variation regularization method (TV) requires the least measurements and consumes the least running time for small-scale reconstruction; the CGD and AP methods run fastest in large-scale cases; the TV and AP methods are the most robust to measurement noise. In a word, there are trade-offs between capture efficiency, computational complexity and robustness to noise among different SPI algorithms. We have released our source code for non-commercial use.

Experimental comparison of single-pixel imaging algorithms

http://arxiv.org/pdf/1707.03164v2

Title Experimental comparison singlepixel imaging algorithm Summary Singlepixel imaging SPI novel technique capturing 2D image using photodiode instead conventional 2D array sensor SPI owns high signaltonoise ratio wide spectrum range low cost robustness light scattering Various algorithm proposed SPI reconstruction including linear correlation method alternating projection method AP compressive sensing based method However comprehensive review discussing respective advantage important SPIs application development paper reviewed compared algorithm unified reconstruction framework Besides proposed two SPI algorithm including conjugate gradient descent based method CGD Poisson maximum likelihood based method simulation experiment validate following conclusion obtain comparable reconstruction accuracy compressive sensing based total variation regularization method TV requires least measurement consumes least running time smallscale reconstruction CGD AP method run fastest largescale case TV AP method robust measurement noise word tradeoff capture efficiency computational complexity robustness noise among different SPI algorithm released source code noncommercial use Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Giovanni Savino', 'Simone Piantini', 'Gustavo Gil', 'Marco Pierini']

1707.03435v2

Research suggests that a Motorcycle Autonomous Emergency Braking system (MAEB) could influence 25% of the crashes involving powered two wheelers (PTWs). By automatically slowing down a host PTW of up to 10 km/h in inevitable collision scenarios, MAEB could potentially mitigate the crash severity for the riders. The feasibility of automatic decelerations of motorcycles was shown via field trials in controlled environment. However, the feasibility of correct MAEB triggering in the real traffic context is still unclear. In particular, MAEB requires an accurate obstacle detection, the feasibility of which from a single track vehicle has not been confirmed yet. To address this issue, our study presents obstacle detection tests in a real-world MAEB-sensitive crash scenario.

Obstacle detection test in real-word traffic contexts for the purposes of motorcycle autonomous emergency braking (MAEB)

http://arxiv.org/pdf/1707.03435v2

Title Obstacle detection test realword traffic context purpose motorcycle autonomous emergency braking MAEB Summary Research suggests Motorcycle Autonomous Emergency Braking system MAEB could influence 25 crash involving powered two wheeler PTWs automatically slowing host PTW 10 kmh inevitable collision scenario MAEB could potentially mitigate crash severity rider feasibility automatic deceleration motorcycle shown via field trial controlled environment However feasibility correct MAEB triggering real traffic context still unclear particular MAEB requires accurate obstacle detection feasibility single track vehicle confirmed yet address issue study present obstacle detection test realworld MAEBsensitive crash 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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['Soo-Chang Pei', 'Shih-Gu Huang']

1707.03688v1

As a generalization of the two-dimensional Fourier transform (2D FT) and 2D fractional Fourier transform, the 2D nonseparable linear canonical transform (2D NsLCT) is useful in optics, signal and image processing. To reduce the digital implementation complexity of the 2D NsLCT, some previous works decomposed the 2D NsLCT into several low-complexity operations, including 2D FT, 2D chirp multiplication (2D CM) and 2D affine transformations. However, 2D affine transformations will introduce interpolation error. In this paper, we propose a new decomposition called CM-CC-CM-CC decomposition, which decomposes the 2D NsLCT into two 2D CMs and two 2D chirp convolutions (2D CCs). No 2D affine transforms are involved. Simulation results show that the proposed methods have higher accuracy, lower computational complexity and smaller error in the additivity property compared with the previous works. Plus, the proposed methods have perfect reversibility property that one can reconstruct the input signal/image losslessly from the output.

Two-dimensional nonseparable discrete linear canonical transform based on CM-CC-CM-CC decomposition

http://arxiv.org/pdf/1707.03688v1

Title Twodimensional nonseparable discrete linear canonical transform based CMCCCMCC decomposition Summary generalization twodimensional Fourier transform 2D FT 2D fractional Fourier transform 2D nonseparable linear canonical transform 2D NsLCT useful optic signal image processing reduce digital implementation complexity 2D NsLCT previous work decomposed 2D NsLCT several lowcomplexity operation including 2D FT 2D chirp multiplication 2D CM 2D affine transformation However 2D affine transformation introduce interpolation error paper propose new decomposition called CMCCCMCC decomposition decomposes 2D NsLCT two 2D CMs two 2D chirp convolution 2D CCs 2D affine transforms involved Simulation result show proposed method higher accuracy lower computational complexity smaller error additivity property compared previous work Plus proposed method perfect reversibility property one reconstruct input signalimage losslessly output Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Soo-Chang Pei', 'Shih-Gu Huang', 'Jian-Jiun Ding']

1707.03689v1

As an extension of the 2D fractional Fourier transform (FRFT) and a special case of the 2D linear canonical transform (LCT), the gyrator transform was introduced to produce rotations in twisted space/spatial-frequency planes. It is a useful tool in optics, signal processing and image processing. In this paper, we develop discrete gyrator transforms (DGTs) based on the 2D LCT. Taking the advantage of the additivity property of the 2D LCT, we propose three kinds of DGTs, each of which is a cascade of low-complexity operators. These DGTs have different constraints, characteristics, and properties, and are realized by different computational algorithms. Besides, we propose a kind of DGT based on the eigenfunctions of the gyrator transform. This DGT is an orthonormal transform, and thus its comprehensive properties, especially the additivity property, make it more useful in many applications. We also develop an efficient computational algorithm to significantly reduce the complexity of this DGT. At the end, a brief review of some important applications of the DGTs is presented, including mode conversion, sampling and reconstruction, watermarking, and image encryption.

Discrete Gyrator Transforms: Computational Algorithms and Applications

http://arxiv.org/pdf/1707.03689v1

Title Discrete Gyrator Transforms Computational Algorithms Applications Summary extension 2D fractional Fourier transform FRFT special case 2D linear canonical transform LCT gyrator transform introduced produce rotation twisted spacespatialfrequency plane useful tool optic signal processing image processing paper develop discrete gyrator transforms DGTs based 2D LCT Taking advantage additivity property 2D LCT propose three kind DGTs cascade lowcomplexity operator DGTs different constraint characteristic property realized different computational algorithm Besides propose kind DGT based eigenfunctions gyrator transform DGT orthonormal transform thus comprehensive property especially additivity property make useful many application also develop efficient computational algorithm significantly reduce complexity DGT end brief review important application DGTs presented including mode conversion sampling reconstruction watermarking image encryption Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Julien Fade', 'Estéban Perrotin', 'Jérôme Bobin']

1707.03705v1

We propose an original concept of compressive sensing (CS) polarimetric imaging based on a digital micro-mirror (DMD) array and two single-pixel detectors. The polarimetric sensitivity of the proposed setup is due to an experimental imperfection of reflecting mirrors which is exploited here to form an original reconstruction problem, including a CS problem and a source separation task. We show that a two-step approach tackling each problem successively is outperformed by a dedicated combined reconstruction method, which is explicited in this article and preferably implemented through a reweighted FISTA algorithm. The combined reconstruction approach is then further improved by including physical constraints specific to the polarimetric imaging context considered, which are implemented in an original constrained GFB algorithm. Numerical simulations demonstrate the efficiency of the 2-pixel CS polarimetric imaging setup to retrieve polarimetric contrast data with significant compression rate and good reconstruction quality. The influence of experimental imperfections of the DMD are also analyzed through numerical simulations, and 2D polarimetric imaging reconstruction results are finally presented.

Two-pixel polarimetric camera by compressive sensing

http://arxiv.org/pdf/1707.03705v1

Title Twopixel polarimetric camera compressive sensing Summary propose original concept compressive sensing CS polarimetric imaging based digital micromirror DMD array two singlepixel detector polarimetric sensitivity proposed setup due experimental imperfection reflecting mirror exploited form original reconstruction problem including CS problem source separation task show twostep approach tackling problem successively outperformed dedicated combined reconstruction method explicited article preferably implemented reweighted FISTA algorithm combined reconstruction approach improved including physical constraint specific polarimetric imaging context considered implemented original constrained GFB algorithm Numerical simulation demonstrate efficiency 2pixel CS polarimetric imaging setup retrieve polarimetric contrast data significant compression rate good reconstruction quality influence experimental imperfection DMD also analyzed numerical simulation 2D polarimetric imaging reconstruction result finally presented Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yan Zhang', 'An Pan', 'Ming Lei', 'Baoli Yao']

1707.03716v1

Fourier ptychographic microscopy (FPM) is a recently proposed computational imaging technique with both high resolution and wide field-of-view. In current FP experimental setup, the dark-field images with high-angle illuminations are easily submerged by stray light and background noise due to the low signal-to-noise ratio, thus significantly degrading the reconstruction quality and also imposing a major restriction on the synthetic numerical aperture (NA) of the FP approach. To this end, an overall and systematic data preprocessing scheme for noise removal from FP's raw dataset is provided, which involves sampling analysis as well as underexposed/overexposed treatments, then followed by the elimination of unknown stray light and suppression of inevitable background noise, especially Gaussian noise and CCD dark current in our experiments. The reported non-parametric scheme facilitates great enhancements of the FP's performance, which has been demonstrated experimentally that the benefits of noise removal by these methods far outweigh its defects of concomitant signal loss. In addition, it could be flexibly cooperated with the existing state-of-the-art algorithms, producing a stronger robustness of the FP approach in various applications.

Data preprocessing methods for robust Fourier ptychographic microscopy

http://arxiv.org/pdf/1707.03716v1

Title Data preprocessing method robust Fourier ptychographic microscopy Summary Fourier ptychographic microscopy FPM recently proposed computational imaging technique high resolution wide fieldofview current FP experimental setup darkfield image highangle illumination easily submerged stray light background noise due low signaltonoise ratio thus significantly degrading reconstruction quality also imposing major restriction synthetic numerical aperture NA FP approach end overall systematic data preprocessing scheme noise removal FPs raw dataset provided involves sampling analysis well underexposedoverexposed treatment followed elimination unknown stray light suppression inevitable background noise especially Gaussian noise CCD dark current experiment reported nonparametric scheme facilitates great enhancement FPs performance demonstrated experimentally benefit noise removal method far outweigh defect concomitant signal loss addition could flexibly cooperated existing stateoftheart algorithm producing stronger robustness FP approach various 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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['Amanda Ramcharan', 'Kelsee Baranowski', 'Peter McCloskey', 'Babuali Ahmed', 'James Legg', 'David Hughes']

1707.03717v2

Cassava is the third largest source of carbohydrates for human food in the world but is vulnerable to virus diseases, which threaten to destabilize food security in sub-Saharan Africa. Novel methods of cassava disease detection are needed to support improved control which will prevent this crisis. Image recognition offers both a cost effective and scalable technology for disease detection. New transfer learning methods offer an avenue for this technology to be easily deployed on mobile devices. Using a dataset of cassava disease images taken in the field in Tanzania, we applied transfer learning to train a deep convolutional neural network to identify three diseases and two types of pest damage (or lack thereof). The best trained model accuracies were 98% for brown leaf spot (BLS), 96% for red mite damage (RMD), 95% for green mite damage (GMD), 98% for cassava brown streak disease (CBSD), and 96% for cassava mosaic disease (CMD). The best model achieved an overall accuracy of 93% for data not used in the training process. Our results show that the transfer learning approach for image recognition of field images offers a fast, affordable, and easily deployable strategy for digital plant disease detection.

Using Transfer Learning for Image-Based Cassava Disease Detection

http://arxiv.org/pdf/1707.03717v2

Title Using Transfer Learning ImageBased Cassava Disease Detection Summary Cassava third largest source carbohydrate human food world vulnerable virus disease threaten destabilize food security subSaharan Africa Novel method cassava disease detection needed support improved control prevent crisis Image recognition offer cost effective scalable technology disease detection New transfer learning method offer avenue technology easily deployed mobile device Using dataset cassava disease image taken field Tanzania applied transfer learning train deep convolutional neural network identify three disease two type pest damage lack thereof best trained model accuracy 98 brown leaf spot BLS 96 red mite damage RMD 95 green mite damage GMD 98 cassava brown streak disease CBSD 96 cassava mosaic disease CMD best model achieved overall accuracy 93 data used training process result show transfer learning approach image recognition field image offer fast affordable easily deployable strategy digital plant disease detection Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['F. Li', 'J. Du']

1707.03720v1

Vision sensors lie in the heart of computer vision. In many computer vision applications, such as AR/VR, non-contacting near-field communication (NFC) with high throughput is required to transfer information to algorithms. In this work, we proposed a novel NFC system which utilizes multiple frequency bands to achieve high throughput.

Multiband NFC for High-Throughput Wireless Computer Vision Sensor Network

http://arxiv.org/pdf/1707.03720v1

Title Multiband NFC HighThroughput Wireless Computer Vision Sensor Network Summary Vision sensor lie heart computer vision many computer vision application ARVR noncontacting nearfield communication NFC high throughput required transfer information algorithm work proposed novel NFC system utilizes multiple frequency band achieve high throughput Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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