Cross Image Inference Scheme for Stereo Matching

  • Xiao Tan
  • Changming Sun
  • Xavier Sirault
  • Robert Furbank
  • Tuan D. Pham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7727)

Abstract

In this paper, we propose a new interconnected Markov Random Field (MRF) or iMRF model for the stereo matching problem. Comparing with the standard MRF, our model takes into account the consistency between the label of a pixel in one image and the labels of its possible matching points in the other image. Inspired by the turbo decoding scheme, we formulate this consistency by a cross image reference term which is iteratively updated in our matching framework. The proposed iMRF model represents the matching problem better than the standard MRF and gives better results even without using any other information from segmentation prior or occlusion detection. We incorporate segmentation information and the coarse-to-fine scheme into our model to further improve the matching performance.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Larsen, E.S., Mordohai, P., Pollefeys, M., Fuchs, H.: Temporally consistent reconstruction from multiple video streams using enhanced belief propagation. In: ICCV, pp. 1–8 (2007)Google Scholar
  2. 2.
    Birchfield, S., Tomasi, C.: Multiway cut for stereo and motion with slanted surfaces. In: ICCV, vol. 1, pp. 489–495. IEEE (1999)Google Scholar
  3. 3.
    Tao, H., Sawhney, H., Kumar, R.: A global matching framework for stereo computation. In: ICCV 2001, vol. 1, pp. 532–539. IEEE (2001)Google Scholar
  4. 4.
    Yang, Q., Wang, L., Yang, R., Stewénius, H., Nistér, D.: Stereo matching with color-weighted correlation, hierarchical belief propagation, and occlusion handling. PAMI 31, 492–504 (2008)CrossRefGoogle Scholar
  5. 5.
    Bleyer, M., Rother, C., Kohli, P.: Surface stereo with soft segmentation. In: CVPR, pp. 1570–1577 (2010)Google Scholar
  6. 6.
    Bleyer, M., Rother, C., Kohli, P., Scharstein, D., Sinha, S.: Object stereo - joint stereo matching and object segmentation. In: CVPR, pp. 3081–3088 (2011)Google Scholar
  7. 7.
    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV 47, 7–42 (2002)MATHCrossRefGoogle Scholar
  8. 8.
    Boykov, Y., Veksler, O., Zabih, R.: A variable window approach to early vision. PAMI 20, 1283–1294 (1998)CrossRefGoogle Scholar
  9. 9.
    Veksler, O.: Stereo correspondence with compact windows via minimum ratio cycle. PAMI 24, 1654–1660 (2002)CrossRefGoogle Scholar
  10. 10.
    Kang, S.B., Szeliski, R., Chai, J.: Handling occlusions in dense multi-view stereo. In: CVPR, vol. 1, pp. 103–110 (2001)Google Scholar
  11. 11.
    Darrell, T.: A radial cumulative similarity transform for robust image correspondence. In: CVPR, pp. 656–662 (1998)Google Scholar
  12. 12.
    Xu, Y., Wang, D., Feng, T., Shum, H.Y.: Stereo computation using radial adaptive windows. In: ICPR, vol. 3, pp. 595–598 (2002)Google Scholar
  13. 13.
    Yoon, K.J., Kweon, I.S.: Adaptive support-weight approach for correspondence search. PAMI 28, 650–656 (2006)CrossRefGoogle Scholar
  14. 14.
    Zitnick, C.L., Kanade, T.: A cooperative algorithm for stereo matching and occlusion detection. PAMI 22, 675–684 (2000)CrossRefGoogle Scholar
  15. 15.
    Ishikawa, H., Geiger, D.: Occlusions, Discontinuities, and Epipolar Lines in Stereo. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, p. 232. Springer, Heidelberg (1998)Google Scholar
  16. 16.
    Bobick, A.F., Intille, S.S.: Large occlusion stereo. IJCV 33, 181–200 (1999)CrossRefGoogle Scholar
  17. 17.
    Wang, L., Yang, R.: Global stereo matching leveraged by sparse ground control points. In: CVPR, pp. 3033–3040 (2011)Google Scholar
  18. 18.
    Xu, L., Jia, J.: Stereo Matching: An Outlier Confidence Approach. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 775–787. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    Geiger, D., Girosi, F.: Parallel and deterministic algorithms from mrfs: Surface reconstruction. PAMI, 401–412 (1991)Google Scholar
  20. 20.
    Sun, C.: Fast stereo matching using rectangular subregioning and 3D maximum-surface techniques. IJCV 47, 99–117 (2002)MATHCrossRefGoogle Scholar
  21. 21.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. PAMI 23, 1222–1239 (2001)CrossRefGoogle Scholar
  22. 22.
    Sun, J., Zheng, N.N., Shum, H.Y.: Stereo matching using belief propagation. PAMI 25, 787–800 (2003)CrossRefGoogle Scholar
  23. 23.
    Sun, J., Li, Y., Kang, S.B., Shum, H.Y.: Symmetric stereo matching for occlusion handling. In: CVPR, vol. 2, pp. 399–407 (2005)Google Scholar
  24. 24.
    Kolmogorov, V., Zabih, R.: Multi-camera Scene Reconstruction via Graph Cuts. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part III. LNCS, vol. 2352, pp. 82–96. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  25. 25.
    Kolmogorov, V., Zabih, R.: Computing visual correspondence with occlusions using graph cuts. In: ICCV 2001, vol. 2, pp. 508–515. IEEE (2001)Google Scholar
  26. 26.
    Egnal, G., Wildes, R.P.: Detecting binocular half-occlusions: Empirical comparisons of five approaches. PAMI 24, 1127–1133 (2002)CrossRefGoogle Scholar
  27. 27.
    Wu, C., Frahm, J., Pollefeys, M.: Repetition-based dense single-view reconstruction. In: CVPR 2011, pp. 3113–3120. IEEE (2011)Google Scholar
  28. 28.
    Berrou, C., Glavieux, A., Thitimajshima, P.: Near Shannon limit error-correcting coding and decoding: Turbo-codes (1). In: IEEE International Conference on Communications, vol. 2, pp. 1064–1070 (1993)Google Scholar
  29. 29.
    McEliece, R.J., MacKay, D.J.C., Cheng, J.F.: Turbo decoding as an instance of Pearl’s belief propagation algorithm. IEEE Journal on Selected Areas in Communications 16, 140–152 (1998)CrossRefGoogle Scholar
  30. 30.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient belief propagation for early vision. IJCV 70, 41–54 (2006)CrossRefGoogle Scholar
  31. 31.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann (1988)Google Scholar
  32. 32.
    Pyndiah, R.M.: Near-optimum decoding of product codes: Block turbo codes. IEEE Transactions on Communications 46, 1003–1010 (1998)MATHCrossRefGoogle Scholar
  33. 33.
    Lehmann, F.: Turbo segmentation of textured images. PAMI 33, 16–29 (2010)CrossRefGoogle Scholar
  34. 34.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24, 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena 60, 259–268 (1992)MATHCrossRefGoogle Scholar
  36. 36.
    Scharstein, D., Szeliski, R. (2011), http://www.vision.middlebury.edu/stereo/
  37. 37.
    Zhang, K., Lu, J., Lafruit, G.: Cross-based local stereo matching using orthogonal integral images. CSVT 19, 1073–1079 (2009)Google Scholar
  38. 38.
    Richardt, C., Orr, D., Davies, I., Criminisi, A., Dodgson, N.A.: Real-time spatiotemporal stereo matching using the dual-cross-bilateral grid. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 510–523. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  39. 39.
    Bleyer, M., Gelautz, M.: A layered stereo algorithm using image segmentation and global visibility constraints. ICIP 5, 2997–3000 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xiao Tan
    • 1
    • 2
  • Changming Sun
    • 2
  • Xavier Sirault
    • 3
  • Robert Furbank
    • 3
  • Tuan D. Pham
    • 4
  1. 1.SEIT of UNSW CanberraCanberraAustralia
  2. 2.CSIRO Mathematics, Informatics and StatisticsNorth RydeAustralia
  3. 3.CSIRO Plant IndustryCanberraAustralia
  4. 4.Aizu Research Cluster for Medical Engineering and InformaticsThe University of AizuFukushimaJapan

Personalised recommendations