Cross image inference scheme for stereo matching

Xiao Tan*, Changming Sun, Xavier Sirault, Robert Furbank, Tuan D. Pham

*Corresponding author for this work

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

1 Citation (Scopus)


In this paper, we propose a new interconnected Markov Random Field (MRF) or iMRF model for the stereo matching problem. Comparing with the standard MRF, our model takes into account the consistency between the label of a pixel in one image and the labels of its possible matching points in the other image. Inspired by the turbo decoding scheme, we formulate this consistency by a cross image reference term which is iteratively updated in our matching framework. The proposed iMRF model represents the matching problem better than the standard MRF and gives better results even without using any other information from segmentation prior or occlusion detection. We incorporate segmentation information and the coarse-to-fine scheme into our model to further improve the matching performance.

Original languageEnglish
Title of host publicationComputer Vision, ACCV 2012 - 11th Asian Conference on Computer Vision, Revised Selected Papers
Number of pages14
EditionPART 4
Publication statusPublished - 2013
Externally publishedYes
Event11th Asian Conference on Computer Vision, ACCV 2012 - Daejeon, Korea, Republic of
Duration: 5 Nov 20129 Nov 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 4
Volume7727 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th Asian Conference on Computer Vision, ACCV 2012
Country/TerritoryKorea, Republic of


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