Segmentation-Based Adaptive Support for Accurate Stereo Correspondence

  • Federico Tombari
  • Stefano Mattoccia
  • Luigi Di Stefano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4872)


Significant achievements have been attained in the field of dense stereo correspondence by local algorithms based on an adaptive support. Given the problem of matching two correspondent pixels within a local stereo process, the basic idea is to consider as support for each pixel only those points which lay on the same disparity plane, rather than those belonging to a fixed support.

This paper proposes a novel support aggregation strategy which includes information obtained from a segmentation process. Experimental results on the Middlebury dataset demonstrate that our approach is effective in improving the state of the art.


Stereo vision stereo matching variable support segmentation 


  1. 1.
    Xu, Y., Wang, D., Feng, T., Shum, H.: Stereo computation using radial adaptive windows. In: Proc. Int. Conf. on Pattern Recognition (ICPR 2002), vol. 3, pp. 595–598 (2002)Google Scholar
  2. 2.
    Boykov, Y., Veksler, O., Zabih, R.: A variable window approach to early vision. IEEE Trans. PAMI 20(12), 1283–1294 (1998)Google Scholar
  3. 3.
    Gong, M., Yang, R.: Image-gradient-guided real-time stereo on graphics hardware. In: Proc. 3D Dig. Imaging and modeling (3DIM), Ottawa, Canada, pp. 548–555 (2005)Google Scholar
  4. 4.
    Hirschmuller, H., Innocent, P., Garibaldi, J.: Real-time correlation-based stereo vision with reduced border errors. Int. Jour. Computer Vision (IJCV) 47(1-3) (2002)Google Scholar
  5. 5.
    Kanade, T., Okutomi, M.: Stereo matching algorithm with an adaptive window: theory and experiment. IEEE Trans. PAMI 16(9), 920–932 (1994)Google Scholar
  6. 6.
    Veksler, O.: Fast variable window for stereo correspondence using integral images. In: Proc. Conf. on Computer Vision and Pattern Recognition (CVPR 2003), pp. 556–561 (2003)Google Scholar
  7. 7.
    Wang, L., Gong, M.W., Gong, M.L., Yang, R.G.: How far can we go with local optimization in real-time stereo matching. In: Proc. Third Int. Symp. on 3D Data Processing, Visualization, and Transmission (3DPVT 2006), pp. 129–136 (2006)Google Scholar
  8. 8.
    Yoon, K.J., Kweon, I.S.: Adaptive support-weight approach for correspondence search. IEEE Trans. PAMI 28(4), 650–656 (2006)Google Scholar
  9. 9.
    Gerrits, M., Bekaert, P.: Local stereo matching with segmentation-based outlier rejection. In: Proc. Canadian Conf. on Computer and Robot Vision (CRV 2006), pp. 66–66 (2006)Google Scholar
  10. 10.
    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)zbMATHCrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    Bleyer, M., Gelautz, M.: A layered stereo matching algorithm using image segmentation and global visibility constraints. Jour. Photogrammetry and Remote Sensing 59, 128–150 (2005)Google Scholar
  13. 13.
    Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Trans. PAMI 24, 603–619 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Federico Tombari
    • 1
    • 2
  • Stefano Mattoccia
    • 1
    • 2
  • Luigi Di Stefano
    • 1
    • 2
  1. 1.Department of Electronics Computer Science and Systems (DEIS), University of Bologna, Viale Risorgimento 2, 40136 - BolognaItaly
  2. 2.Advanced Research Center on Electronic Systems (ARCES), University of Bologna, Via Toffano 2/2, 40135 - BolognaItaly

Personalised recommendations