Advertisement

Stereo Matching Using Belief Propagation

  • Jian Sun
  • Heung-Yeung Shum
  • Nan-Ning Zheng
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2351)

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.

Keywords

Belief Propagation Stereo Vision Stereo Match Line Process Markov Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Download to read the full conference paper text

References

  1. 1.
    P.N. Belhumeur. A bayesian-approach to binocular stereopsis. IJCV, 19(3):237–260, 1996.CrossRefGoogle Scholar
  2. 2.
    S. Birchfield and C. Tomasi. A pixel dissimilarity measure that is insensitive to image sampling. PAMI, 20(4):401–406, 1998.Google Scholar
  3. 3.
    M.J. Black and A. Rangarajan. On the unification of line processes, outlier rejection, and robust statistics with applications in early vision. IJCV, 19(1):57–91, 1996.CrossRefGoogle Scholar
  4. 4.
    A. Blake and A Zisserman. Visual reconstruction. MIT Press, 1987.Google Scholar
  5. 5.
    A.F. Bobick and S.S. Intille. Large occlusion stereo. IJCV, 33(3):1–20, 1999.CrossRefGoogle Scholar
  6. 6.
    Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. ICCV, 1999.Google Scholar
  7. 7.
    D. Comaniciu and P. Meer. Robust analysis of feature spaces: Color image segmentation. CVPR, 1997.Google Scholar
  8. 8.
    I.J. Cox, S.L. Hingorani, S.B. Rao, and B.M. Maggs. A maximum-likelihood stereo algorithm. CVIU, 63(3):542–567, 1996.Google Scholar
  9. 9.
    W.T. Freeman, E.C. Pasztor, and O.T. Carmichael. Learning low-level vision. IJCV, 40(1):25–47, 2000.zbMATHCrossRefGoogle Scholar
  10. 10.
    D. Geiger and F. Girosi. Parallel and deterministic algorithms from mrfs: Surface reconstruction. PAMI, 13(5):401–412, 1991.Google Scholar
  11. 11.
    D. Geiger, B. Ladendorf, and A. Yuille. Occlusions and binocular stereo. IJCV, 14(3):211–226, 1995.CrossRefGoogle Scholar
  12. 12.
    S. Geman and D. Geman. Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. PAMI, 6(6):721–741, 1984.zbMATHGoogle Scholar
  13. 13.
    H. Hirschmueller. Improvements in real-time correlation-based stereo vision. IEEE Workshop on Stereo and Multi-Baseline Vision, 2001.Google Scholar
  14. 14.
    B.K.P. Horn and M.J. Brooks. The variational approach to shape from shading. CVGIP, 33(2):174–208, 1986.Google Scholar
  15. 15.
    H. Ishikawa and D. Geiger. Occlusions, discontinuities, and epipolar lines in stereo. ECCV, 1998.Google Scholar
  16. 16.
    T. Kanade and M. Okutomi. A stereo matching algorithm with an adaptive window: Theory and experiment. PAMI, 16(9):920–932, 1994.Google Scholar
  17. 17.
    V. Kolmogorov and R. Zabih. Computing visual correspondence with occlusions via graph cuts. ICCV, 2001.Google Scholar
  18. 18.
    S. Osher L.I. Rudin and E. Fatemi. Nonlinear total variation based noise removal algorithms. Physica D, 27(60):259–268, 1992.Google Scholar
  19. 19.
    Judea Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Mateo, California, 1988.Google Scholar
  20. 20.
    D. Scharstein and R. Szeliski. Stereo matching with nonlinear diffusion. IJCV, 28(2):155–174, 1998.CrossRefGoogle Scholar
  21. 21.
    D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV, 47(1):7–42, 2002.zbMATHCrossRefGoogle Scholar
  22. 22.
    H. Tao, H.S. Sawhney, and R. Kumar. A global matching framework for stereo computation. ICCV, 2001.Google Scholar
  23. 23.
    O. Veksler. Stereo matching by compact windows via minimum ratio cycle. ICCV, 2001.Google Scholar
  24. 24.
    W. T. Yedidia, J. S. Freeman and Weiss Y. Bethe free energy, kikuchi approximations, and belief propagation algorithms. Technical Report TR-2001-16, Mitsubishi Electric Reseach, 2001.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Jian Sun
    • 1
    • 2
  • Heung-Yeung Shum
    • 2
  • Nan-Ning Zheng
    • 1
  1. 1.Artificial Intelligence and Robotics LabXi’an Jiaotong UniversityChina
  2. 2.Visual Computing GroupMicrosoft Research AsiaBeijing

Personalised recommendations

Cite paper