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Near Real-Time Stereo Matching Using Geodesic Diffusion

Near Real-Time Stereo Matching Using Geodesic Diffusion. Leonardo De- Maeztu , Arantxa Villanueva, Member, IEEE, and Rafael Cabeza. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 34, NO. 2, FEBRUARY 2012. Guan-Yu Liu. Outline. Introduction Overview Related work

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Near Real-Time Stereo Matching Using Geodesic Diffusion

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  1. Near Real-Time Stereo Matching Using Geodesic Diffusion Leonardo De-Maeztu, Arantxa Villanueva, Member, IEEE, and Rafael Cabeza IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 34, NO. 2, FEBRUARY 2012 • Guan-Yu Liu

  2. Outline • Introduction • Overview • Related work • Method • Experimental Results • CUDA • Q & A

  3. Introduction(1/4) • Stereo matching • Local matching • A finite region(window size) is being computed • Global matching • Do smoothness by energy minimization techniques

  4. Introduction(2/4) • When using local support regions, it is implicitly assumed that all pixels in the region are of the same depth. • the fronto-parallel surfaces assumption • Adaptive-weight methods

  5. Introduction(3/4) • Adaptive-weight methods are the local algorithms yielding the best results. • Highly time-consuming task • Anisotropic diffusion, a computer vision technique very similar to adaptive weighting but computationally less expensive. • a computer vision technique very similar to adaptive weighting but computationally less expensive.

  6. Introduction(4/4) • Geodesic diffusion is inspired by anisotropic diffusion. • diffusing both matching costs and weights. • Near real-time execution is demonstrated using a commercial graphics card.

  7. Related Work • Adaptive-weight methods [7] • Adaptive-weight methods [8] • Anisotropic diffusion [9] [7] K.-J. Yoon and I.S. Kweon, “Adaptive Support-Weight Approach for Correspondence Search,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 4, pp. 650-656, Apr. 2006. [8] A. Hosni, M. Bleyer, M. Gelautz, and C. Rhemann, “Local Stereo Matching Using Geodesic Support Weights,” Proc. Int’l Conf. Image Processing, pp. 2093-2096, 2009. [9] P. Perona and J. Malik, “Scale-Space and Edge Detection Using Anisotropic Diffusion,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 629-639, July 1990.

  8. Related Work(1/6) • Adaptive-weight methods [7]

  9. Related Work(2/6) • Adaptive-weight methods [7] • 123 • 123 • 123 Euclidean distance between the values in the CIELab color space and spatial euclidean distance truncated absolute difference (TAD)

  10. Related Work(3/6) • Adaptive-weight methods [8]

  11. Related Work(4/6) • Adaptive-weight methods [8] • 123 • 123 • 123 Shortest path

  12. Related Work(5/6) • The two algorithms use the same optimization technique, winner-takes-all (WTA).

  13. Related Work(6/6) • Anisotropic diffusion is a computer vision technique similar to bilateral filtering. • only the comparison of each pixel with its immediate neighbors is necessary.

  14. Method • A : Anisotropic diffusion • B : Geodesic diffusion

  15. Method.A(1/3) • Anisotropic diffusion

  16. Method.A(2/3) • Anisotropic diffusion • 123 • 123 Euclidean distance between the values in the CIELab color space

  17. Method.A(3/3) • It is aniterative computer vision technique.[9]

  18. Method • A : Anisotropic diffusion • B : Geodesic diffusion

  19. Method.B(1/8) • Three principles • Costs and weights are diffused so that the importance of each cost value is known in each iteration. • In each iteration, the costs and weights at each pixel are accumulated. After the last iteration, all the support region information has been accumulated at each pixel. • To increase the efficiency of information diffusion and to avoid loops, turns in the direction of diffusion are penalized.

  20. Method.B(2/8) • Geodesic diffusion

  21. Method.B(3/8) • Each of the four positions inherits the costs and weights of each of the four direct neighbors of each pixel.

  22. Method.B(4/8) • Geodesic diffusion • 123 • 123 • 123

  23. Method.B(5/8) • i= 0 right neighbors • i = 1 lower neighbors • i = 2 upper neighbors • i = 3 left neighbors

  24. Method.B(6/8) • The cost and weight information derived from a direct neighbor is not returned to this neighbor. • Costs are only propagated with their full weights in the same direction of their propagation direction in the previous iteration.

  25. Method.B(7/8) • Geodesic diffusion • 123 • 123

  26. Method.B(8/8) • At the end of the diffusion process, the DSI costs are normalized. • Thus, concluded, and the disparity map is then computed by selecting the lower cost disparity for each pixel WTA.

  27. Experimental Results(1/8)

  28. Experimental Results(2/8)

  29. Experimental Results(3/8)

  30. Experimental Results(4/8)

  31. Experimental Results(5/)

  32. Experimental Results(6/8)

  33. Experimental Results(7/8)

  34. Experimental Results(8/8)

  35. CUDA • CUDA implementation of our algorithm ran in less than 60 milliseconds for the Tsukuba stereo pair on a GeForce 480 GTX card.

  36. Q & A

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