Stereo Matching with Color-Weighted Correlation, Hierarchical Belief Propagation, and Occlusion Handling

Authors:
Qingxiong Yang;Liang Wang;Ruigang Yang;Henrik Stewénius;David Nistér
Affiliations:
University of Illinois at Urbana Champaign, Urbana;University of Kentucky, Lexington;University of Kentucky, Lexington;Google Switzerland, Zurich;Microsoft Corp., Redmond
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2009

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Abstract

In this paper, we formulate a stereo matching algorithm with careful handling of disparity, discontinuity and occlusion. The algorithm works with a global matching stereo model based on an energy-minimization framework. The global energy contains two terms, the data term and the smoothness term. The data term is first approximated by a color-weighted correlation, then refined in occluded and low-texture areas in a {\it{repeated}} application of a hierarchical loopy belief propagation algorithm. The experimental results are evaluated on the Middlebury data sets, showing that our algorithm is the {\it{top}} performer among all the algorithms listed there.