@inproceedings{05f7e0177b77420fa1ac76ced95dc959,
title = "Stereo matching using belief propagation",
abstract = "In this paper, we formulate the stereo matching problem as a Markov network consisting of three coupled Markov random fields (MRF{\textquoteright}s). These three MRF{\textquoteright}s model a smooth field for depth/disparity, a line process for depth discontinuity and a binary process for occlusion, respectively. After eliminating the line process and the binary process by introducing two robust functions, we obtain the maximum a posteriori (MAP) estimation in the Markov network by applying a Bayesian belief propagation (BP) algorithm. Furthermore, we extend our basic stereo model to incorporate other visual cues (e.g., image segmentation) that are not modeled in the three MRF{\textquoteright}s, and again obtain the MAP solution. Experimental results demonstrate that our method outperforms the state-of-art stereo algorithms for most test cases.",
author = "Jian Sun and Shum, \{Heung Yeung\} and Zheng, \{Nan Ning\}",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2002.; 7th European Conference on Computer Vision, ECCV 2002 ; Conference date: 28-05-2002 Through 31-05-2002",
year = "2002",
doi = "10.1007/3-540-47967-8\_34",
language = "英语",
isbn = "9783540437444",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "510--524",
editor = "Anders Heyden and Gunnar Sparr and Mads Nielsen and Peter Johansen",
booktitle = "Computer Vision - 7th European Conference on Computer Vision, ECCV 2002, Proceedings",
}