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Stereo matching using belief propagation
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.
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Research works printed but not linked: 34
- Baker, Depth from Edge and Intensity Based Stereo
- 10.1007/BF00055146 · DOI · Paper page
- 10.1109/34.677269 · DOI · Paper page
- 10.1007/BF00131148 · DOI · Paper page
- 10.7551/mitpress/7132.001.0001 · DOI · Paper page
- Bobick, 1999, Large Occlusion Stereo, Int’l J. Computer Vision, 33, 1
- Bolles, The Jisct Stereo Evaluation
- 10.1109/ICCV.1999.791245 · DOI · Paper page
- 10.1109/CVPR.1997.609410 · DOI
- 10.1006/cviu.1996.0040 · DOI
- 10.1023/A:1026501619075 · DOI · Paper page
- 10.1109/34.134040 · DOI · Paper page
- 10.1007/BF01679683 · DOI · Paper page
- 10.1109/TPAMI.1984.4767596 · DOI · Paper page
- 10.1201/b14835 · DOI · Paper page
- 10.1109/SMBV.2001.988772 · DOI
- 10.1016/0734-189X(86)90114-3 · DOI · Paper page
- 10.1007/BFb0055670 · DOI · Paper page
- 10.1007/978-94-011-5014-9 · DOI · Paper page
- 10.1109/34.310690 · DOI · Paper page
- Kang, Handling Occlusions in Dense Multi-View Stereo
- 10.1109/iccv.2001.937668 · DOI · Paper page
- Osher, 1992, Nonlinear Total Variation Based Noise Removal Algorithms, Physica D, 27, 259
- 10.1007/BF01451740 · doi:10.1007/bf01451740
- 10.1126/science.968482 · DOI · Paper page
- Mumford, 1988, Optimal Approximations by Piecewise Smooth Functions and Variational Problems, Comm. Pure and Applied Math.
- Pearl, 1988, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
- 10.1117/12.7972515 · DOI
- 10.1023/A:1008015117424 · DOI · Paper page
- Scharstein, 2002, A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms, Int’l J. Computer Vision, 47, 7, 10.1023/A:1014573219977 · DOI · Paper page
- 10.1145/280814.280882 · DOI · Paper page
- 10.1109/ICCV.2001.937562 · DOI · Paper page
- 10.1109/ICCV.2001.937563 · DOI
- 10.1007/BFb0028345 · DOI · Paper page
Cited by
A Database and Evaluation Methodology for Optical Flow
Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., & Szeliski, R. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-010-0390-2 · Cited by 2,205
“A number of recent approaches use discrete optimization algorithms, similar to those employed in stereo matching, such as graph cuts [14] and belief propagation [71].”
MethodLearning Conditional Random Fields for Stereo
Scharstein, D., & Pal, C. (2007) · 2007 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2007.383191 · Cited by 851
Roth, S., & Black, M. J. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-008-0197-6 · Cited by 847
Deformable Model Fitting by Regularized Landmark Mean-Shift
Saragih, J. M., Lucey, S., & Cohn, J. F. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-010-0380-4 · Cited by 787
A Database and Evaluation Methodology for Optical Flow
Baker, S., Roth, S., Scharstein, D., Black, M. J., Lewis, J. P., & Szeliski, R. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4408903 · Cited by 618
Metadata
- Authors
- Sun, J., Zheng, N.-N., & Shum, H.-Y.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2003-07
- Volume
- 25
- Issue
- 7
- Pages
- 787-800
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2151646056
- Cited by
- 1,271
- References
- 0
- FWCI
- 37.52
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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