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Titlebook: Energy Minimization Methods in Computer Vision and Pattern Recognition; Third International Mário Figueiredo,Josiane Zerubia,Anil K. Jain

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书目名称Energy Minimization Methods in Computer Vision and Pattern Recognition
副标题Third International
编辑Mário Figueiredo,Josiane Zerubia,Anil K. Jain
视频video
概述Includes supplementary material:
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Energy Minimization Methods in Computer Vision and Pattern Recognition; Third International  Mário Figueiredo,Josiane Zerubia,Anil K. Jain
描述This volume consists of the 42 papers presented at the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR2001),whichwasheldatINRIA(InstitutNationaldeRechercheen Informatique et en Automatique) in Sophia Antipolis, France, from September 3 through September 5, 2001. This workshop is the third of a series, which was started with EMMCVPR’97, held in Venice in May 1997, and continued with EMMCVR’99, which took place in York, in July 1999. Minimization problems and optimization methods permeate computer vision (CV), pattern recognition (PR), and many other ?elds of machine intelligence. The aim of the EMMCVPR workshops is to bring together people with research interests in this interdisciplinary topic. Although the subject is traditionally well represented at major international conferences on CV and PR, the EMMCVPR workshops provide a forum where researchers can report their recent work and engage in more informal discussions. We received 70 submissions from 23 countries, which were reviewed by the members of the program committee. Based on the reviews, 24 papers were - cepted for oral presentation and 18 for poster presentation. I
出版日期Conference proceedings 2001
关键词Computer Vision; Energy minimization; Markov random fields; clustering; hidden Markov models; image class
版次1
doihttps://doi.org/10.1007/3-540-44745-8
isbn_softcover978-3-540-42523-6
isbn_ebook978-3-540-44745-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2001
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Definitions and Clinical Presentation,given functional subject to some noise constraints. A MAP estimator which uses a Markov or a maximum entropy random field model for a prior distribution can be viewed as a minimizer of a variational problem. Using notions from robust statistics, a variational filter called . is proposed. It yields t
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https://doi.org/10.1007/978-3-030-33691-2. coming from a real distance) is also very important to allow fast retrieval on large databases. Moreover, these similarity functions should be flexible enough to be tuned to fit users behaviour. These two constraints, . and . are generally difficult to fulfill. Our contribution is two folds: We sh
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R. Lorenz,H. Kanaya,R. D. Nagpal,J. Iizukaocedures if Bayesian principles are used. Heuristic approaches have been used instead in practical applications..This paper tries to overcome this difficulty by proposing an algorithm which is derived from sound theoretical principles and fast. This algorithm is based on the expansion of the noise p
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