Multi-label Moves for MRFs with Truncated Convex Priors

Optimization with graph cuts became very popular in recent years. While exact optimization is possible in a few cases, many useful energy functions are NP hard to optimize. One approach to approximate optimization is the so-called move making algorithms. At each iteration, a move-making algorithm ma...

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Bibliographic Details
Published in:	International journal of computer vision Vol. 98; no. 1; pp. 1 - 14
Main Author:	Veksler, Olga
Format:	Journal Article
Language:	English
Published:	Boston Springer US 01.05.2012 Springer Springer Nature B.V
Subjects:	Algorithmics. Computability. Computer arithmetics Algorithms Analysis Applied sciences Approximation Artificial Intelligence Boolean Computer Imaging Computer Science Computer science; control theory; systems Computer vision Energy Exact sciences and technology Graph theory Graphs Image Processing and Computer Vision Information retrieval. Graph Labels Magnetorheological fluids Markov analysis Mathematical optimization Optimization Optimization techniques Pattern Recognition Pattern Recognition and Graphics Pattern recognition. Digital image processing. Computational geometry Pixels Studies Switches Theoretical computing Vision Vision systems Truncated convex priors Discrete optimization Markov random fields (MRF) Graph cuts Markov process Probabilistic approach Image processing Energy function Graph theory Image restoration Optimization Graph cut NP hard problem Truncated shape Discrete programming Adulteration
ISSN:	0920-5691, 1573-1405
Online Access:	Get full text
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