Stereo matching using belief propagation

Jian Sun; Heung Yeung Shum; Nan Ning Zheng

doi:10.1007/3-540-47967-8_34

Stereo matching using belief propagation

Jian Sun, Heung Yeung Shum, Nan Ning Zheng

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

97 Citations (Scopus)

Abstract

In this paper, we formulate the stereo matching problem as a Markov network consisting of three coupled Markov random fields (MRF’s). These three MRF’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’s, and again obtain the MAP solution. Experimental results demonstrate that our method outperforms the state-of-art stereo algorithms for most test cases.

Original language	English
Title of host publication	Computer Vision - 7th European Conference on Computer Vision, ECCV 2002, Proceedings
Editors	Anders Heyden, Gunnar Sparr, Mads Nielsen, Peter Johansen
Publisher	Springer Verlag
Pages	510-524
Number of pages	15
ISBN (Print)	9783540437444
DOIs	https://doi.org/10.1007/3-540-47967-8_34
Publication status	Published - 2002
Externally published	Yes
Event	7th European Conference on Computer Vision, ECCV 2002 - Copenhagen, Denmark Duration: 28 May 2002 → 31 May 2002

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2351
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th European Conference on Computer Vision, ECCV 2002
Country/Territory	Denmark
City	Copenhagen
Period	28/05/02 → 31/05/02

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2002.

Access to Document

10.1007/3-540-47967-8_34

Cite this

Sun, J., Shum, H. Y., & Zheng, N. N. (2002). Stereo matching using belief propagation. In A. Heyden, G. Sparr, M. Nielsen, & P. Johansen (Eds.), Computer Vision - 7th European Conference on Computer Vision, ECCV 2002, Proceedings (pp. 510-524). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2351). Springer Verlag. https://doi.org/10.1007/3-540-47967-8_34

Sun, Jian ; Shum, Heung Yeung ; Zheng, Nan Ning. / Stereo matching using belief propagation. Computer Vision - 7th European Conference on Computer Vision, ECCV 2002, Proceedings. editor / Anders Heyden ; Gunnar Sparr ; Mads Nielsen ; Peter Johansen. Springer Verlag, 2002. pp. 510-524 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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 = "English",

isbn = "9783540437444",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Sun, J, Shum, HY & Zheng, NN 2002, Stereo matching using belief propagation. in A Heyden, G Sparr, M Nielsen & P Johansen (eds), Computer Vision - 7th European Conference on Computer Vision, ECCV 2002, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2351, Springer Verlag, pp. 510-524, 7th European Conference on Computer Vision, ECCV 2002, Copenhagen, Denmark, 28/05/02. https://doi.org/10.1007/3-540-47967-8_34

Stereo matching using belief propagation. / Sun, Jian; Shum, Heung Yeung; Zheng, Nan Ning.
Computer Vision - 7th European Conference on Computer Vision, ECCV 2002, Proceedings. ed. / Anders Heyden; Gunnar Sparr; Mads Nielsen; Peter Johansen. Springer Verlag, 2002. p. 510-524 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2351).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

T1 - Stereo matching using belief propagation

AU - Sun, Jian

AU - Shum, Heung Yeung

AU - Zheng, Nan Ning

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2002.

PY - 2002

Y1 - 2002

N2 - In this paper, we formulate the stereo matching problem as a Markov network consisting of three coupled Markov random fields (MRF’s). These three MRF’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’s, and again obtain the MAP solution. Experimental results demonstrate that our method outperforms the state-of-art stereo algorithms for most test cases.

AB - In this paper, we formulate the stereo matching problem as a Markov network consisting of three coupled Markov random fields (MRF’s). These three MRF’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’s, and again obtain the MAP solution. Experimental results demonstrate that our method outperforms the state-of-art stereo algorithms for most test cases.

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SN - 9783540437444

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Computer Vision - 7th European Conference on Computer Vision, ECCV 2002, Proceedings

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Sun J, Shum HY, Zheng NN. Stereo matching using belief propagation. In Heyden A, Sparr G, Nielsen M, Johansen P, editors, Computer Vision - 7th European Conference on Computer Vision, ECCV 2002, Proceedings. Springer Verlag. 2002. p. 510-524. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-47967-8_34

Stereo matching using belief propagation

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