Stereo matching using belief propagation
Top Cited Papers
- 20 June 2003
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 25 (7) , 787-800
- https://doi.org/10.1109/tpami.2003.1206509
Abstract
In this paper, we formulate the stereo matching problem as a Markov network and solve it using Bayesian belief propagation. The stereo Markov network consists of three coupled Markov random fields that model the following: a smooth field for depth/disparity, a line process for depth discontinuity, and a binary process for occlusion. After eliminating the line process and the binary process by introducing two robust functions, we apply the belief propagation algorithm to obtain the maximum a posteriori (MAP) estimation in the Markov network. Other low-level visual cues (e.g., image segmentation) can also be easily incorporated in our stereo model to obtain better stereo results. Experiments demonstrate that our methods are comparable to the state-of-the-art stereo algorithms for many test cases.Keywords
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