Dense Stereo Correspondence with Contrast Context Histogram, Segmentation-Based Two-Pass Aggregation and Occlusion Handling

  • Tianliang Liu
  • Pinzheng Zhang
  • Limin Luo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)


In a local and perceptual organization framework, a novel stereo correspondence algorithm is proposed to provide dense and accurate disparity maps under point ambiguity. First, the initial matching technique is based on raw matching cost obtained from local descriptor with contrast context histogram and two-pass cost aggregation via segmentation-based adaptive support weight. Second, the disparity estimation procedure consists sequentially of two steps: namely, a narrow occlusion handling and a multi-directional weighted least square (WLS) fitting for large occlusion. The experiment results indicate that our algorithm can increase robustness against outliers, and then obtain comparable and accurate disparity than other local stereo methods effectively, and it is even better than some algorithms using advanced and offline but computationally complicated global optimization based algorithms.


Stereo vision stereo matching local descriptor segmentation parallel computing weighted least square large occlusion 


  1. 1.
    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. Jour. Computer Vision (IJCV) 47(1/2/3), 7–42 (2002), CrossRefzbMATHGoogle Scholar
  2. 2.
    Yoon, K.J., Kweon, I.S.: Adaptive support-weight approach for correspondence search. IEEE Trans. PAMI 28, 650–656 (2006)CrossRefGoogle Scholar
  3. 3.
    Min, D.B., Sohn, K.: Cost aggregation and occlusion handling with WLS in stereo matching. IEEE Trans. IP 17(8), 1431–1442 (2008)MathSciNetGoogle Scholar
  4. 4.
    Gu, Z., Su, X.Y., Liu, Y.K., Zhang, Q.C.: Local stereo matching with adaptive support-weight, rank transform and disparity calibration. Pattern Recognition Letters (PRL 2008) 29, 1230–1235 (2008)CrossRefGoogle Scholar
  5. 5.
    Tombari, F., Mattoccia, S., Di Stefano, L.: Segmentation-based adaptive support for accurate stereo correspondence. In: Mery, D., Rueda, L. (eds.) PSIVT 2007. LNCS, vol. 4872, pp. 427–438. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Gong, M.L., Yang, R.G., Wang, L., Gong, M.W.: A performance study on different cost aggregation approaches used in real-time stereo matching. Int. Jour. Computer Vision (IJCV) 75(2), 283–296 (2007)CrossRefGoogle Scholar
  7. 7.
    Yoon, K.J., Kweon, I.S.: Stereo matching with the distinctive similarity measure. In: Proc. Int. Conf. on Computer Vision (ICCV 2007), pp. 1–7 (2007)Google Scholar
  8. 8.
    Tombari, F., Mattoccia, S., Di Stefano, L., Addimanda, E.: Near real-time stereo based on effective cost aggregation. In: Proc. Int. Conf. on Pattern Recognition (ICPR 2008) (2008)Google Scholar
  9. 9.
    Brockers, R., Hund, M., Mertsching, B.: Stereo vision using cost-relaxation with 3D support regions. In: Image and Vision Computing New Zealand (IVCNZ 2005) (2005)Google Scholar
  10. 10.
    Lu, J.B., Lafruit, G., Catthoor, F.: Anisotropic local high-confidence voting for accurate stereo correspondence. In: Proc. SPIE, vol. 6812 (2008)Google Scholar
  11. 11.
    Mordohai, P., Medioni, G.: Stereo using monocular cues within the tensor voting framework. IEEE Trans. PAMI 28(6), 968–982 (2006)CrossRefzbMATHGoogle Scholar
  12. 12.
    Kolmogorov, V., Zabih, R.: Computing visual correspondence with occlusions using graph cuts. In: Proc. Int. Conf. on Computer Vision (ICCV 2001), pp. 508–515 (2001)Google Scholar
  13. 13.
    Klaus, A., Sormann, M., Karner, K.: Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure. In: Proc. Int. Conf. on Pattern Recognition (ICPR 2006), vol. 3, pp. 15–18 (2006)Google Scholar
  14. 14.
    Taguchi, Y., Wilburn, B., Zitnick, C.L.: Stereo reconstruction with mixed pixels using adaptive over-segmentation. In: Proc. Int. Conf. on Computer Vision and Pattern Recognition (CVPR 2008), pp. 2720–2727 (2008)Google Scholar
  15. 15.
    Veksler, O.: Stereo correspondence by dynamic programming on a tree. In: Proc. Int. Conf. on Computer Vision and Pattern Recognition (CVPR 2005), pp. 384–390 (2005)Google Scholar
  16. 16.
    Wang, Z.F., Zheng, Z.G.: A region based stereo matching algorithm using cooperative optimization. In: Proc. Int. Conf. on Computer Vision and Pattern Recognition (CVPR 2008), pp. 887–894 (2008)Google Scholar
  17. 17.
    Woodford, O.J., Torr, P.H.S., Reid, I.D., Fitzgibbon, A.W.: Global stereo reconstruction under second order smoothness priors. In: Proc. Int. Conf. on Computer Vision and Pattern Recognition (CVPR 2008), pp. 2570–2577 (2008)Google Scholar
  18. 18.
    Hirschmuller, H.: Stereo processing by semiglobal matching and mutual information. IEEE Trans. PAMI 30(2), 328–341 (2008)CrossRefGoogle Scholar
  19. 19.
    Mattoccia, S., Tombari, F., Di Stefano, L.: Stereo vision enabling precise border localization within a scanline optimization framework. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part II. LNCS, vol. 4844, pp. 517–527. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  20. 20.
    Huang, C.R., Chen, C.S., Chung, P.C.: Contrast context histogram–an efficient discriminating local descriptor for object recognition and image matching. Pattern Recognition (PR 2008) 41(10), 3071–3077 (2008)CrossRefzbMATHGoogle Scholar
  21. 21.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. PAMI 24(4), 509–522 (2002)CrossRefGoogle Scholar
  22. 22.
    Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Trans. PAMI 24(5), 603–619 (2002)CrossRefGoogle Scholar
  23. 23.
    Oh, J.D., Ma, S.W., Kuo, C.-C.J.: Stereo matching via disparity estimation and surface modeling. In: Proc. Int. Conf. on Computer Vision and Pattern Recognition (CVPR 2007), pp. 1696–1703 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Tianliang Liu
    • 1
  • Pinzheng Zhang
    • 1
  • Limin Luo
    • 1
  1. 1.Lab of Image Science and Technology (LIST)Southeast UniversityNanjingChina

Personalised recommendations