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Reseach Article

New Global Formulation for a Bilateral based Stereo Matching Algorithm

by Doaa A. Altantawy, Marwa Obbaya, Sherif S. Kishk
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 98 - Number 8
Year of Publication: 2014
Authors: Doaa A. Altantawy, Marwa Obbaya, Sherif S. Kishk

Doaa A. Altantawy, Marwa Obbaya, Sherif S. Kishk . New Global Formulation for a Bilateral based Stereo Matching Algorithm. International Journal of Computer Applications. 98, 8 ( July 2014), 21-28. DOI=10.5120/17205-7419

@article{ 10.5120/17205-7419,
author = { Doaa A. Altantawy, Marwa Obbaya, Sherif S. Kishk },
title = { New Global Formulation for a Bilateral based Stereo Matching Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 98 },
number = { 8 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 21-28 },
numpages = {9},
url = { },
doi = { 10.5120/17205-7419 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:25:41.730656+05:30
%A Doaa A. Altantawy
%A Marwa Obbaya
%A Sherif S. Kishk
%T New Global Formulation for a Bilateral based Stereo Matching Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 98
%N 8
%P 21-28
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

In this paper, a new hybrid local-global stereo matching algorithm (BFGc) is proposed. BFGc makes the maximum benefit from both the introduced local and the global approaches representing the main two stage of the algorithm. Globally, a new energy formulation of the stereo problem in segment domain is proposed which basically depends on the reliability of the disparity estimates results from the adopted local approach, unlike what is typical in global methods. For increasing reliability of the local approach, a new gradient masks is supporting the adopted similarity measure and Bilateral filter, with its edge preserving sense, is adopted for more proper disparity assignment. In segment domain, a plan fitting technique is introduced which aims at inferring all valid planes in disparity space and producing a good initialization for the global optimization space which aims at assigning memberships to the these planes to all pixels in the reference image. The experimental results on the Middleburry dataset demonstrate that our approach stands as a strong candidate with the modern stereo matching algorithms.

  1. Scharstein, D. and Szeliski, R. 2002. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, Int. J. Comput. Vis. 47 (1/2/3) (2002) 7–42.
  2. Yang, Q. 2013. Hardware-efficient bilateral filtering for stereo matching. IEEE Trans. on PAMI, 36(5) 1026 - 1032 .
  3. Richardt, C. , Orr, D. , Davies, I. , Criminisi, A. , and Dodgson, N. A. (2010). Real-time spatiotemporal stereo matching using the dual-cross-bilateral grid. In Proc. Of ECCV , 510-523. Springer Berlin Heidelberg.
  4. Boykov, Y. , Veksler, O. and Zabih, R. 2001. Fast Approx- imate Energy Minimization via Graph Cuts. IEEE Trans. PAMI. 23 (2001)1222-1239.
  5. Hong, L. and Chen, G. 2004. Segment-Based Stereo Matching Using Graph-Cuts. In Proc. of CVPR, (1)74- 81.
  6. Klaus, A. , Sourmann, M. and Karner, K. 2006. Segment- Based Stereo Matching Using Belief Propagation and a Self Adapting Dissimilarity Measure. In Proc. Of ICPR, 15-18
  7. Yang, Q. , Wang, L. , Yang, R. , Stewenius, H. and Nister, D. 2009. Stereo Matching with Color-Weighted Correlation, Hierarchical Belief Propagation, and Occlusion Handling. IEEE Trans. on PAMI, (3) 492-504.
  8. Tappen, M. and Freeman, W. 2003. Comparison of Graph Cuts with Belief Propagation for Stereo. In Proc. Of ICCV, (1)508-515.
  9. Bleyer, M. , Rother, C. , Kohli, P. , Scharstein, D. and Sinha, S. 2011. Object stereo - joint stereo matching and object segmentation. In Proc. Of CVPR, 3081-3088
  10. W. Daolei, K. Lim, Obtaining depth map from segment-based stereo matching using graph cuts, J. Vis. Commun. Image R. 22 (2011) 325–331
  11. Mei, X. , Sun, X. , Dong, W. , Wang, H. , and Zhang, X. 2013. Segment-Tree based Cost Aggregation for Stereo Matching. In Proc. Of CVPR, 313-320.
  12. Tomasi, C. and Manduchi, R. 1998. Bilateral filtering for gray and color images. In Proc. Of ICCV, 839-846
  13. Kanade , T. and Okutomi, M. 1994. A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiments. IEEE Trans. PAMI, 16(9) (1994), 920–932.
  14. Boykov, Y. , Veksler, O. , and Zabih, R. 1998. A Variable Window Approach to Early Vision," IEEE Trans. Pattern Analysis and Machine Intelligence. 20(12)(1998), 1283–1294.
  15. Fusiello, A. , Roberto, V. , and Trucco, E. 1997. Efficient Stereo with Multiple Windowing. In Proc. Of CVPR, 858–863.
  16. Bobick, A. F. , and Intille, S. S. 1999. Large Occlusion Stereo. Int. J. Computer Vision. 33(3)(1999), 181–200.
  17. Kang, S. B. , Szeliski, R. , and Jinxjang, C. 2001. Handling Occlusions in Dense Multi-View Stereo. In Proc. Of CVPR, 1(2001),103–110.
  18. De-Maeztu, L. , Villanueva, A. , Cabeza, R. 2011. Stereo matching using gradient similarity and locally adaptive support-weight, J. Pattern Recognition Letters. 32(13) (2011), 1643-1651.
  19. Yoon, K. -J. and Kweon, I. S. 2006. Adaptive support-weight approach for correspondence search, IEEE Trans. on PAMI, 28(4) (2006) 650–656.
  20. Gerrits, M. and Bekaert, P. 2006. Local stereo matching with segmentation-based outlier rejection. In Proc. of IEEE 3rd Canadian Conference in Computer and Robot Vision, 66-66
  21. Rhemann, C. , Hosni, A. , Bleyer, M. , Rother, C. , and Gelautz, M. 2011. Fast cost-volume filtering for visual correspondence and beyond. In Proc. Of CVPR, 3017-3024.
  22. Rhemann, C. , Bleyer, M. , Rother, C. 2011. PatchMatch stereo - stereo matching with slanted support windows. In Proc. Of BMVC, (11) 1-11.
  23. Mattoccia, S. , Giardino, S. , and Gambini, A. (2010). Accurate and efficient cost aggregation strategy for stereo correspondence based on approximated joint bilateral filtering. In Proc. Of ACCV, 371-380. Springer Berlin Heidelberg.
  24. Yang, Q. 2012. Recursive bilateral filtering. In Proc. Of ECCV, 399-413. Springer Berlin Heidelberg.
  25. Kolmogorov, V. and Zabih, R. 2001. Computing visual correspondence with occlusions using graph cuts. In Proc. Of ICCV, (2)508-515.
  26. Birchfield, S. and Tomasi, C. 1999. Multiway cut for stereo and motion with slanted surfaces. In Proc. Of ICCV, 489–495.
  27. Bleyer, M. and Gelautz, M. 2006. Graph-based surface reconstruction from stereo pairs using image segmentation. In Proc. Of SPIE 5665, 288–299.
  28. Zuliani, M. RANSAC for Dummies, Vision Research Lab, University of California, Santa Barbara (2009).
  29. Comaniciu, D. and Meer, P. 2002. Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5) (2002) 603–619.
  30. Farid, H. , Simoncelli, E. 2004. Differentiation of discrete multidimensional signals. IEEE Trans. Signal Process. 13(4) (2004) 496-508.
  31. Scharstein, D. and Szeliski, R. Middlebury stereo evalu-ation- version 2, http://vision. middlebury. edu/stereo/eval.
  32. Pham, C. C. , Ha, S. V. U. , and Jeon, J. W. 2012. Adaptive guided image filtering for sharpness enhancement and noise reduction. In Advances in Image and Video Technology , 323-334. Springer Berlin Heidelberg.
Index Terms

Computer Science
Information Sciences


Stereo matching Self-adapting similarity measure Color segmentation Graph cuts