Stereo Matching by Using Self-distributed Segmentation and Massively Parallel GPU Computing

  • Wenbao QiaoEmail author
  • Jean-Charles Créput
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9693)


As an extension of using image segmentation to do stereo matching, firstly, by using self-organizing map (som) and K-means algorithms, this paper provides a self-distributed segmentation method that allocates segments according to image’s texture changement where in most cases depth discontinuities appear. Then, for stereo, under the fact that the segmentation of left image is not exactly same with the segmentation of right image, we provide a matching strategy that matches segments of left image to pixels of right image as well as taking advantage of border information from these segments. Also, to help detect occluded regions, an improved aggregation cost that considers neighbor valid segments and their matching characteristics is provided. For post processing, a gradient border based median filter that considers the closest adjacent valid disparity values instead of all pixels’ disparity values within a rectangle window is provided. As we focus on real-time execution, these time-consumming works for segmentation and stereo matching are executed on a massively parallel cellular matrix GPU computing model. Finaly, we provide our visual dense disparity maps before post processing and final evaluation of sparse results after post-processing to allow comparison with several ranking methods top listed on Middlebury.


Stereo Image segmentation SOM Self-distributed segments 



This paper is sponsored by China Scholarship Council(CSC) and laboratory IRTES-SET of UTBM.


  1. 1.
    Cigla, C., Alatan, A.A.: Information permeability for stereo matching. Sig. Process. Image Commun. 28(9), 1072–1088 (2013)CrossRefGoogle Scholar
  2. 2.
    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1–3), 7–42 (2002)CrossRefzbMATHGoogle Scholar
  3. 3.
    Egnal, G.: Mutual information as a stereo correspondence measure. Technical reports (CIS), p. 113 (2000)Google Scholar
  4. 4.
    Kim, J., Kolmogorov, V., Zabih, R.: Visual correspondence using energy minimization and mutual information. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, pp. 1033–1040. IEEE (2003)Google Scholar
  5. 5.
    Zitnick, C.L., Kang, S.B.: Stereo for image-based rendering using image over-segmentation. Int. J. Comput. Vis. 75(1), 49–65 (2007)CrossRefGoogle Scholar
  6. 6.
    Klaus, A., Sormann, M., Karner, K.: Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 3, pp. 15–18. IEEE (2006)Google Scholar
  7. 7.
    Yoon, K.-J., Kweon, I.S.: Adaptive support-weight approach for correspondence search. IEEE Trans. Pattern Anal. Mach. Intell. 4, 650–656 (2006)CrossRefGoogle Scholar
  8. 8.
    Scharstein, D., Hirschmüller, H., Kitajima, Y., Krathwohl, G., Nešić, N., Wang, X., Westling, P.: High-resolution stereo datasets with subpixel-accurate ground truth. In: Jiang, X., Hornegger, J., Koch, R. (eds.) GCPR 2014. LNCS, vol. 8753, pp. 31–42. Springer, Heidelberg (2014)Google Scholar
  9. 9.
    Hirschmüller, H., Innocent, P.R., Garibaldi, J.: Real-time correlation-based stereo vision with reduced border errors. Int. J. Comput. Vis. 47(1–3), 229–246 (2002)CrossRefzbMATHGoogle Scholar
  10. 10.
    NVIDIA: CUDA C Programming Guide 4.2, CURAND Library, Profiler User’s Guide (2012).
  11. 11.
    Bentley, J.L., Weide, B.W., Yao, A.C.: Optimal expected-time algorithms for closest point problems. ACM Trans. Math. Softw. (TOMS) 6(4), 563–580 (1980)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)CrossRefGoogle Scholar
  13. 13.
    Bruzzone, L., Carlin, L.: A multilevel context-based system for classification of very high spatial resolution images. IEEE Trans. Geosci. Remote Sens. 44(9), 2587–2600 (2006)CrossRefGoogle Scholar
  14. 14.
    Xiao, J., Xia, L., Lin, L.: Segment-based stereo matching using edge dynamic programming. In: 2010 3rd International Congress on Image and Signal Processing (CISP), vol. 4, pp. 1676–1679. IEEE (2010)Google Scholar
  15. 15.
    Gerrits, M., Bekaert, P.: Local stereo matching with segmentation-based outlier rejection. In: The 3rd Canadian Conference on Computer and Robot Vision, 2006, p. 66. IEEE (2006)Google Scholar
  16. 16.
    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
  17. 17.
    Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.IRTES-SETUniversity of Technology of Belfort-MontbéliardBelfortFrance

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