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Titlebook: Energy Minimization Methods in Computer Vision and Pattern Recognition; 10th International C Xue-Cheng Tai,Egil Bae,Marius Lysaker Conferen

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书目名称Energy Minimization Methods in Computer Vision and Pattern Recognition
副标题10th International C
编辑Xue-Cheng Tai,Egil Bae,Marius Lysaker
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Energy Minimization Methods in Computer Vision and Pattern Recognition; 10th International C Xue-Cheng Tai,Egil Bae,Marius Lysaker Conferen
描述This volume constitutes the refereed proceedings of the 10th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2015, held in Hong Kong, China, in January 2015. The 36 revised full papers were carefully reviewed and selected from 45 submissions. The papers are organized in topical sections on discrete and continuous optimization; image restoration and inpainting; segmentation; PDE and variational methods; motion, tracking and multiview reconstruction; statistical methods and learning; and medical image analysis.
出版日期Conference proceedings 2015
关键词clustering algorithm; computer vision; convex optimization; image processing; iterative methods; machine
版次1
doihttps://doi.org/10.1007/978-3-319-14612-6
isbn_softcover978-3-319-14611-9
isbn_ebook978-3-319-14612-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2015
The information of publication is updating

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Maximizing Flows with Message-Passing: Computing Spatially Continuous Min-Cutsion. We focus on the convex relaxed formulations and investigate the corresponding flow-maximization based dual formulations. We propose a series of novel continuous max-flow models based on evaluating different constraints of flow excess, where the classical pre-flow and pseudo-flow models over gra
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Low Rank Priors for Color Image Regularization is a desirable prior for multichannel image restoration, we propose a novel scheme of minimizing the rank of the image Jacobian and extend this idea to second derivatives in the framework of total generalized variation. We compare the proposed convex and nonconvex relaxations of the rank function b
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