Globally optimal solutions for energy minimization in stereo vision using reweighted belief propagation

Talya Meltzer*, Chen Yanover, Yair Weiss

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

94 Scopus citations

Abstract

A wide range of low level vision problems have been formulated in terms of finding the most probable assignment of a Markov Random Field (or equivalently the lowest energy configuration). Perhaps the most successful example is stereo vision. For the stereo problem, it has been shown that finding the global optimum is NP hard but good results have been obtained using a number of approximate optimization algorithms. In this paper we show that for standard benchmark stereo pairs, the global optimum can be found in about 30 minutes using a variant of the belief propagation (BP) algorithm. We extend previous theoretical results on reweighted belief propagation to account for possible ties in the beliefs and using these results we obtain easily checkable conditions that guarantee that the BP disparities are the global optima. We verify experimentally that these conditions are typically met for the standard benchmark stereo pairs and discuss the implications of our results for further progress in stereo.

Original languageAmerican English
Title of host publicationProceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005
Pages428-435
Number of pages8
DOIs
StatePublished - 2005
EventProceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005 - Beijing, China
Duration: 17 Oct 200520 Oct 2005

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
VolumeI

Conference

ConferenceProceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005
Country/TerritoryChina
CityBeijing
Period17/10/0520/10/05

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