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arXiv 2017年

作者： Zhang, Wuming Zhao, Xi Morvan, Jean-Marie Chen, Liming Laboratory of LIRIS Department of Mathematic and Computer Science Ecole Centrale de Lyon Ecully69310 France School of Management Xi'an Jiaotong University Xi'an710049 China Universite Lyon 1 Institut Camille Jordan CNRS UMR 5208 43 blvd du 11 Novembre 1918 Villeurbanne-CedexF-69622 France King Abdullah University of Science and Technology Visual Computing Center Bldg 1 Thuwal23955-6900 Saudi Arabia

2D face analysis techniques, such as face landmarking, face recognition and face verification, are reasonably dependent on illumination conditions which are usually uncontrolled and unpredictable in the real world. An illumination robust preprocessing method thus remains a significant challenge in reliable face analysis. In this paper we propose a novel approach for improving lighting normalization through building the underlying reflectance model which characterizes interactions between skin surface, lighting source and camera sensor, and elaborates the formation of face color appearance. Specifically, the proposed illumination processing pipeline enables the generation of Chromaticity Intrinsic Image (CII) in a log chromaticity space which is robust to illumination variations. Moreover, as an advantage over most prevailing methods, a photo-realistic color face image is subsequently reconstructed which eliminates a wide variety of shadows whilst retaining the color information and identity details. Experimental results under different scenarios and using various face databases show the effectiveness of the proposed approach to deal with lighting variations, including both soft and hard shadows, in face recognition. Copyright © 2017, The Authors. All rights reserved.

关键词： Lighting

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A Survey of Blue-Noise Sampling and Its Applications

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Journal of computer science & Technology 2015年第3期30卷 439-452页

作者：严冬明郭建伟王斌张晓鹏 Peter Wonk Visual Computing Center King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Software Tsinghua University Beijing 100084 China Department of Computer Science and Engineering Arizona State University Tempe AZ 85287 U.S.A.

In this paper, we survey recent approaches to blue-noise sampling and discuss their beneficial applications. We discuss the sampling algorithms that use points as sampling primitives and classify the sampling algorithms based on various aspects, e.g., the sampling domain and the type of algorithm. We demonstrate several well-known applications that can be improved by recent blue-noise sampling techniques, as well as some new applications such as dynamic sampling and blue-noise remeshing.

关键词： blue-noise sampling Poisson-disk sampling Lloyd relaxation rendering remeshing

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Color Object Recognition via Cross-Domain Learning on RGB-D Images

Color Object Recognition via Cross-Domain Learning on RGB-D ...

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IEEE International Conference on Robotics and Automation

作者： Yawen Huang Fan Zhu Ling Shao Alejandro F. Frangi the Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB) Department of Electronic and Electrical Engineering The University of Sheffield Sheffield United Kingdom the Multimedia and Visual Computing Lab Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi the Computer Vision and Artificial Intelligence Group Department of Computer Science and Digital Technologies Northumbria University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9781467380270

This paper addresses the object recognition problem using multiple-domain inputs. We present a novel approach that utilizes labeled RGB-D data in the training stage, where depth features are extracted for enhancing the discriminative capability of the original learning system that only relies on RGB images. The highly dissimilar source and target domain data are mapped into a unified feature space through transfer at both feature and classifier levels. In order to alleviate cross-domain discrepancy, we employ a state-of-the-art domain-adaptive dictionary learning algorithm that updates image representations in both domains and the classifier parameters simultaneously. The proposed method is trained on a RGB-D Object dataset and evaluated on the Caltech-256 dataset. Experimental results suggest that our approach can lead to significant performance gain over the state-of-the-art methods.

关键词： RGB-D enhancing discriminative capability

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Guest editors' foreword

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International Journal of Computational Geometry and Applications 2014年第4期24卷 259-260页

作者： Ahn, Hee-Kap Vigneron, Antoine Department of Computer Science and Engineering Pohang University of Science and Technology (POSTECH) 77 Cheongam-Ro Nam-Gu Pohang Gyeongbuk South Korea Visual Computing Center King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia

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Wasserstein propagation for semi-supervised learning 31

Wasserstein propagation for semi-supervised learning

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31st International Conference on Machine Learning, ICML 2014

作者： Solomon, Justin Rustamov, Raif M. Guibas, Leonidas Butscher, Adrian Department of Computer Science Stanford University 353 Serra Mall StanfordCA94305 United States Max Planck Center for Visual Computing and Communication Campus El 4 Saarbrücken66123 Germany

ISBN: (纸本)9781634393973

2014 Probability distributions and histograms are natural representations for product ratings, traffic measurements, and other data considered in many machine learning applications. Thus, this paper introduces a technique for graph-based semi- supervised learning of histograms, derived from the theory of optimal transportation. Our method has several properties making it suitable for this application;in particular, its behavior can be characterized by the moments and shapes of the histograms at the labeled nodes. In addition, it can be used for histograms on non-standard domains like circles, revealing a strategy for manifold-valued semi-supervised learning. We also extend this technique to related problems such as smoothing distributions on graph nodes.

关键词： Probability distributions

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Distinction between handwritten and machine-printed text based on the bag of visual words model

Distinction between handwritten and machine-printed text bas...

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作者： Zagoris, Konstantinos Pratikakis, Ioannis Antonacopoulos, Apostolos Gatos, Basilis Papamarkos, Nikos Visual Computing Group Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Research Lab School of Computing Science and Engineering University of Salford Greater Manchester United Kingdom Institute of Informatics and Telecommunications National Center for Scientific Research Demokritos Athens Greece

In a variety of documents, ranging from forms to archive documents and books with annotations, machine printed and handwritten text may coexist in the same document image, raising significant issues within the recognition pipeline. It is, therefore, necessary to separate the two types of text so that it becomes feasible to apply different recognition methodologies to each modality. In this paper, a new approach is proposed which strives towards identifying and separating handwritten from machine printed text using the Bag of visual Words model (BoVW). Initially, blocks of interest are detected in the document image. For each block, a descriptor is calculated based on the BoVW. The final characterization of the blocks as Handwritten, Machine Printed or Noise is made by a decision scheme which relies upon the combination of binary SVM classifiers. The promising performance of the proposed approach is shown by using a consistent evaluation methodology which couples meaningful measures along with new datasets dedicated to the problem upon consideration. © 2013 Elsevier Ltd. All rights reserved.

关键词： Support vector machines

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A simple efficient interior point method for min-cost flow

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8889卷 753-765页

作者： Becker, Ruben Karrenbauer, Andreas Max Planck Institute for Informatics Saarbrucken Germany Max-Planck-Center for Visual Computing and Communication Saarbrucken Germany Saarbrücken Graduate School for Computer Science Saarbrucken Germany

ISBN: (纸本)9783319130743

We present a novel simpler method for the min-cost flow problem and prove that its expected running time is bounded by Õ(m3/2). This matches the best known bounds, which have previously been achieved only by far more complex algorithms or by algorithms for special cases. Our contribution contains three algorithmic parts that are interesting in their own right: (1) We provide a linear time construction of an equivalent auxiliary network and interior primal and dual points, i.e. flows, node potentials and slacks, with potential P0 = Õ(equation found). (2)We present a potential reduction algorithm that transforms initial solutions of potential P0 to ones with duality gap below 1 in Õ (P0 ・ CEF(n, m, Ε)) time, where Ε −1 = O(m2) and CEF(n, m, Ε) denotes the running time of any algorithm that computes an Ε-approximate electrical flow. (3) We show that, taking solutions with duality gap less than 1 as input, one can compute optimal integral node potentials in O(m + n log n) time with our novel crossover procedure. Altogether, using a variant of a state-ofthe- art Ε-electrical flow solver, we obtain a new simple algorithm for the min-cost flow problem running in Õ (m3/2). © Springer International Publishing Switzerland 2014.

关键词： Costs

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Discrete visual Perception

Discrete Visual Perception

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International Conference on Pattern Recognition

作者： Nikos Paragios Nikos Komodakis Center for Visual Computing Ecole Centrale de Paris Chatenay-Malabry France Ecole de Ponts-ParisTech LIGM (Gaspard Monge Computer Science Laboratory) Champs-sur-Marne France

Computational vision and biomedical image have made tremendous progress of the past decade. This is mostly due the development of efficient learning and inference algorithms which allow better, faster and richer modeling of visual perception tasks. Graph-based representations are among the most prominent tools to address such perception through the casting of perception as a graph optimization problem. In this paper, we briefly introduce the interest of such representations, discuss their strength and limitations and present their application to address a variety of problems in computer vision and biomedical image analysis.

关键词： Graphical models Computational modeling Optimization Context Biomedical imaging computer vision Biological system modeling

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Creating optimal code for GPU-accelerated CT reconstruction using ant colony optimization

Creating optimal code for GPU-accelerated CT reconstruction ...

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作者： Papenhausen, Eric Zheng, Ziyi Mueller, Klaus Computer Science Department Center of Visual Computing Stony Brook University Stony Brook NY 11794-4400 United States

Purpose: CT reconstruction algorithms implemented on the GPU are highly sensitive to their implementation details and the hardware they run on. Fine-tuning an implementation for optimal performance can be a time consuming task and require many updates when the hardware changes. There are some techniques that do automatic fine-tuning of GPU code. These techniques, however, are relatively narrow in their fine-tuning and are often based on heuristics which can be inaccurate. The goal of this paper is to present a framework that will automate the process of code optimization with maximum flexibility and produce a final result that is efficient and readable to the user. Methods: The authors propose a method that is able to tune high level implementation details by using the ant colony optimization algorithm to find the optimal implementation in a relatively short amount of time. Our framework does this by taking as input, a file that describes a graph, such that a path through this graph represents a potential implementation. They then use the ant colony optimization algorithm to find the optimal path through this graph based on the execution time and the quality of the image. Results: Two experimental studies are carried out. Using the presented framework, they optimize the performance of a GPU accelerated FDK backprojection implementation and a GPU accelerated separable footprint backprojection implementation. The authors demonstrate that the resulting optimal implementation can be different depending on the hardware specifications. They then compare the results of the framework produced with the results produced by manual optimization. Conclusions: The framework they present is a useful tool for increasing programmer productivity and reducing the overhead of leveraging hardware specific resources. By performing an intelligent search, our framework produces a more efficient image reconstruction implementation in a shorter amount of time. © 2013 American Association of

关键词： Ant colony optimization

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