Cross image inference scheme for stereo matching

Authors:
Xiao Tan;Changming Sun;Xavier Sirault;Robert Furbank;Tuan D. Pham
Affiliations:
SEIT of UNSW Canberra, Canberra, ACT, Australia,CSIRO Mathematics, Informatics and Statistics, North Ryde, NSW, Australia;CSIRO Mathematics, Informatics and Statistics, North Ryde, NSW, Australia;CSIRO Plant Industry, Canberra, ACT, Australia;CSIRO Plant Industry, Canberra, ACT, Australia;Aizu Research Cluster for Medical Engineering and Informatics, The University of Aizu, Fukushima, Japan
Venue:
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Year:
2012

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Abstract

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.