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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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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/e/image/310343.jpg
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https://doi.org/10.1007/3-540-44745-8Computer Vision; Energy minimization; Markov random fields; clustering; hidden Markov models; image class
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978-3-540-42523-6Springer-Verlag Berlin Heidelberg 2001
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R. Lorenz,H. Kanaya,R. D. Nagpal,J. Iizukarobability density function as a Taylor series, un the vicinity of the maximum likelihood estimates, leading to a linear set of equations which are easily solved by standard techniques. Reconstruction examples with synthetic and medical data are provided to evaluate the proposed algorithm.
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0302-9743 s 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 Ven
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Definitions and Clinical Presentation,ropose an information-theoretic gradient descent flow whose functional turns out to be a compromise between a neg-entropy variational integral and a total variation. Illustrating examples demonstrate a much improved performance of the proposed filters in the presence of Gaussian and heavy tailed noise.
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Mycotoxins: How Many Deaths in Africa?hbourhood. Experiments showthat such approach is more efficient for regular textures described by different characteristic long-range interactions than for stochastic textures with overlapping close-range neighbourhoods.
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