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Stereo Matching Based on Dissimilar Intensity Support and Belief Propagation

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Abstract

A novel algorithm based on the window construction method using local edge detection is presented. Firstly, in order to construct the adaptive window, a virtual closed edge is formed around each pixel via second order differential operator. Secondly, a novel rule called Dissimilar Intensity Support (DIS) technique is proposed. This rule is used to distinguish support pixels with dissimilar intensity from those with similar intensity for each centered pixel. So that the performance of window-based cost aggregation computation is improved. Thirdly, belief propagation (BP) optimization algorithm is used to obtain the disparity. The experimental results based on Middlebury stereo benchmark show that the proposed algorithm has good performances.

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References

  1. Tappen, M.F., Freeman, W.T.: Comparison of graph cuts with belief propagation for stereo, using identical MRF parameters. In: International Conference on Computer Vision, pp. 900–907 (2003)

    Chapter  Google Scholar 

  2. Klaus, A., Sormann, M., Karner, K.: Segment-based stereo matching using belief propagation and a self adapting dissimilarity measure. Int. Conf. Pattern Recognit. 3, 15–18 (2006)

    Google Scholar 

  3. Yang, Q., Wang, L., Yang, R., Stewenius, H., Nister, D.: Stereo matching with color-weighted correlation, hierarchical belief propagation, and occlusion handling. IEEE Trans. Pattern Anal. Mach. Intell. 31, 492–504 (2009)

    Article  Google Scholar 

  4. Kanade, T., Okutomi, M.: A stereo matching algorithm with an adaptive window: theory and experiment. IEEE Trans. Pattern Anal. Mach. Intell. 16, 920–932 (1994)

    Article  Google Scholar 

  5. Zhang, K., Lu, J., Lafruit, G.: Scalable stereo matching with locally adaptive polygon approximation. In: International Conference on Image Processing, pp. 313–316 (2008)

    Google Scholar 

  6. Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient belief propagation for early vision. In: IEEE Conference on Computer and Pattern Recognition, vol. 1, pp. 261–268 (2004)

    Google Scholar 

  7. Grauer-Gray, S., Kambhamettu, C.: Hierarchical belief propagation to reduce search space using CUDA for stereo and motion estimation. In: Proceedings of IEEE Workshop on Applications of Computer Vision, pp. 1–8 (2009)

    Google Scholar 

  8. Yoon, K., Kweon, I.S.: Distinctive similarity measure for stereo matching under point ambiguity. Comput. Vis. Image Underst. 112, 173–183 (2008)

    Article  Google Scholar 

  9. Yoon, K., Kweon, I.S.: Adaptive support-weight approach for correspondence search. IEEE Trans. Pattern Anal. Mach. Intell. 28, 650–656 (2006)

    Article  Google Scholar 

  10. Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47, 7–42 (2002)

    Article  MATH  Google Scholar 

  11. Lu, J., Lafruit, G., Catthoor, F.: Local high-confidence voting for accurate stereo correspondence. In: Proceedings of the SPIE, p. 6812 (2008)

    Google Scholar 

  12. http://vision.middlebury.edu/stereo/

  13. Sun, J., Zheng, N., Shum, H.: Stereo matching using belief propagation. IEEE Trans. Pattern Anal. Mach. Intell. 25(7), 787–800 (2003)

    Article  Google Scholar 

  14. Hosni, A., Bleyer, M., Gelautz, M., Rhemann, C.: Local stereo matching using geodesic support weights. In: International Conference on Image Processing, pp. 2093–2096 (2009)

    Google Scholar 

  15. Gu, Z., Su, X., Liu, Y.: Local stereo matching with adaptive support-weight, rank transform and disparity calibration. Pattern Recognit. Lett. 29(9), 1230–1235 (2008)

    Article  Google Scholar 

  16. Baker, S., Sim, T., Kanade, T.: A characterization of inherent stereo ambiguities. In: Proceedings of 8th IEEE International Conference on Computer Vision, pp. 428–437 (2001)

    Google Scholar 

  17. Brockers, R.: Cooperative stereo matching with color-based adaptive local support. In: Proceedings of 13th International Conference on Computer Analysis of Images and Patterns, pp. 1019–1027 (2009)

    Chapter  Google Scholar 

  18. Zhang, K., Lu, J., Lafruit, G.: Cross-based local stereo matching using orthogonal integral images. IEEE Trans. Circuits Syst. Video Technol. 19(7), 1073–1079 (2009)

    Article  Google Scholar 

  19. Lu, J., Lafruit, G., Catthoor, F.: Anisotropic local high-confidence voting for accurate stereo correspondence. In: Proceedings of Image Processing: Algorithms and Systems (2008)

    Google Scholar 

  20. Nalpantidis, L., Gasteratos, A.: Biologically and psychophysically inspired adaptive support weights algorithm for stereo correspondence. Robot. Auton. Syst. 58(5), 457–464 (2010)

    Article  Google Scholar 

  21. Nalpantidis, L., Gasteratos, A.: Stereo vision for robotic applications in the presence of non-ideal lighting conditions. Image Vis. Comput. 28(6), 940–951 (2010)

    Article  Google Scholar 

  22. Montserrat, T., Civit, J., Escoda, O.: Depth estimation based on multiview matching with depth/color segmentation and memory efficient belief propagation. In: Proceedings of 16th IEEE International Conference on Image Processing, pp. 2353–2356 (2009)

    Google Scholar 

  23. Banno, A., Ikeuchi, K.: Disparity map refinement and 3D surface smoothing via directed anisotropic diffusion. In: Proceedings of 12th IEEE International Conference on Computer Vision Workshops, pp. 1870–1877 (2009)

    Google Scholar 

  24. Yang, Q., Wang, L., Ahuja, N.: A constant-space belief propagation algorithm for stereo matching. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1458–1465 (2010)

    Google Scholar 

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Correspondence to Feipeng Da.

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Da, F., He, F. & Chen, Z. Stereo Matching Based on Dissimilar Intensity Support and Belief Propagation. J Math Imaging Vis 47, 27–34 (2013). https://doi.org/10.1007/s10851-013-0448-1

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