Multi-label moves for MRFs with truncated convex priors

DOI10.1007/s11263-011-0491-6zbMath1254.68286OpenAlexW1997530236MaRDI QIDQ1931575

Olga Veksler

Publication date: 15 January 2013

Published in: International Journal of Computer Vision (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11263-011-0491-6

zbMATH Keywords

discrete optimizationgraph cutsMarkov random fields (MRF)truncated convex priors

Mathematics Subject Classification ID

Programming involving graphs or networks (90C35) Convex programming (90C25) Pattern recognition, speech recognition (68T10) Machine vision and scene understanding (68T45)

Related Items (2)

GRMA: generalized range move algorithms for the efficient optimization of MRFs ⋮ A survey and comparison of discrete and continuous multi-label optimization approaches for the Potts model

Uses Software

• MAXFLOW

Cites Work

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• New algorithms for convex cost tension problem with application to computer vision
• A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Computer Vision - ECCV 2004

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