A Top-Down Approach to the Estimation of Depth Maps Driven by Morphological Segmentations

  • Jean-Charles Bricola
  • Michel Bilodeau
  • Serge Beucher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9082)


Given a pair of stereo images, the spatial coordinates of a scene point can be derived from its projections onto the two considered image planes. Finding the correspondences between such projections however remains the main difficulty of the depth estimation problem: the matching of points across homogeneous regions is ambiguous and occluded points cannot be matched as their projections do not exist in one of the image planes.

Instead of searching for dense point correspondences, this article proposes an approach to the estimation of depth map which is based on the matching of regions. The matchings are performed at two segmentation levels obtained by morphological criteria which ensure the existence of an hierarchy between the coarse and fine partitions. The hierarchy is then exploited in order to compute fine regional disparity maps which are accurate and free from noisy measurements.

We finally show how this method fits to different sorts of stereo images: those which are highly textured, taken under constant illumination such as Middlebury and those which relevant information resides in the contours only.


Watershed Segmentation hierarchies Disparity estimation Joint stereo segmentation Non-ideal stereo imagery 


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  1. 1.
    Middlebury stereo database,
  2. 2.
    Armstrong, M.: Basic linear geostatistics. Springer (1998)Google Scholar
  3. 3.
    Beucher, S.: Segmentation d’Images et Morphologie Mathématique. Ph.D. thesis, Ecole Nationale Supérieure des Mines de Paris (1990)Google Scholar
  4. 4.
    Bleyer, M., Gelautz, M.: A layered stereo matching algorithm using image segmentation and global visibility constraints. ISPRS Journal of Photogrammetry and Remote Sensing 59(3), 128–150 (2005)CrossRefGoogle Scholar
  5. 5.
    De-Maeztu, L., Villanueva, A., Cabeza, R.: Near real-time stereo matching using geodesic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(2), 410–416 (2012)CrossRefGoogle Scholar
  6. 6.
    Fua, P.: A parallel stereo algorithm that produces dense depth maps and preserves image features. Machine Vision and Applications 6(1), 35–49 (1993)CrossRefGoogle Scholar
  7. 7.
    Goshtasby, A.: Similarity and dissimilarity measures. In: Image Registration. Advances in Computer Vision and Pattern Recognition, Springer, London (2012)CrossRefGoogle Scholar
  8. 8.
    Hirschmuller, H.: Stereo processing by semiglobal matching and mutual information. IEEE Transactions on Pattern Analysis and Machine Intelligence (2008)Google Scholar
  9. 9.
    Jaccard, P.: Bulletin de la société vaudoise des sciences naturelles. Tech. rep. (1901)Google Scholar
  10. 10.
    Prince, S.: Models for grids. In: Computer Vision: Models, Learning, and Inference. Cambridge University Press (2012)Google Scholar
  11. 11.
    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision (2002)Google Scholar
  12. 12.
    Vilaplana, V., Marques, F., Salembier, P.: Binary partition trees for object detection. IEEE Transactions on Image Processing 17(11), 2201–2216 (2008)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Yamaguchi, K., Hazan, T., McAllester, D., Urtasun, R.: Continuous markov random fields for robust stereo estimation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 45–58. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  14. 14.
    Yang, Q., Wang, L., Yang, R., Stewénius, H., Nistér, D.: Stereo matching with color-weighted correlation, hierarchical belief propagation, and occlusion handling. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(3) (2009)Google Scholar
  15. 15.
    Zitnick, C.L., Kang, S.B.: Stereo for image-based rendering using image over-segmentation. International Journal of Computer Vision 75(1), 49–65 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jean-Charles Bricola
    • 1
  • Michel Bilodeau
    • 1
  • Serge Beucher
    • 1
  1. 1.CMM – Centre de Morphologie MathématiqueMINES ParisTech – PSL Research UniversityFontainebleauFrance

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