Markov random fields for vision and image processing

Markov random fields for vision and image processing [electronic resource] / edited by Andrew Blake, Pushmeet Kohli, and Carsten Rother.

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State-of-the-art research on MRFs, successful MRF applications, and advanced topics for future study.

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Bibliographic Details
Online Access: Full Text (via MIT Press)
Other Authors: Blake, Andrew, 1956-, Kohli, Pushmeet, Rother, Carsten
Format: Electronic eBook
Language:English
Published: Cambridge, Mass. : MIT Press, ©2011.
Subjects:
Image processing > Mathematics.
Computer graphics > Mathematics.
Computer vision > Mathematics.
Markov random fields.

MARC

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505 0 0 |g Machine generated contents note:  |g 1.  |t Introduction to Markov Random Fields /  |r Pushmeet Kohli --  |g I.  |t Algorithms for Inference of MAP Estimates for MRFs --  |g 2.  |t Basic Graph Cut Algorithms /  |r Vladimir Kolmogorov --  |g 3.  |t Optimizing Multilabel MRFs Using Move-Making Algorithms /  |r Ramin Zabih --  |g 4.  |t Optimizing Multilabel MRFs with Convex and Truncated Convex Priors /  |r Olga Veksler --  |g 5.  |t Loopy Belief Propagation, Mean Field Theory, and Bethe Approxlmations /  |r Alan Yuille --  |g 6.  |t Linear Programming and Variants of Belief Propagation /  |r Talya Meltzer --  |g II.  |t Applications of MRFs, Including Segmentation --  |g 7.  |t Interactive Foreground Extraction: Using Graph Cut /  |r Andrew Blake --  |g 8.  |t Continuous-Valued MRF for Image Segmentation /  |r Rene Vidal --  |g 9.  |t Bilayer Segmentation of Video /  |r Vladimir Kolmogorov --  |g 10.  |t MRFs for Superresolution and Texture Synthesis /  |r Ce Liu --  |g 11.  |t Comparative Study of Energy Minimization Methods for MRFs. 
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650 0 |a Computer vision  |x Mathematics. 
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