1. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 34, NO. 2, FEBRUARY 2012 1 Leonardo De-Maeztu, Arantxa Villanueva, Member, IEEE, and Rafael Cabeza Guan-Yu Liu

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

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

4. Introduction (2/4) 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) 5  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) 6  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 7  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) 8  Adaptive-weight methods [7]

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

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

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

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

13. Related Work (6/6) 13  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 14  A : Anisotropic diffusion  B : Geodesic diffusion

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

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

17. Method.A (3/3) 17  It is an iterative computer vision technique.[9]

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

19. Method.B (1/8) 19  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) 20  Geodesic diffusion

21. Method.B (3/8) 21  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) 22  Geodesic diffusion  123

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

24. Method.B (6/8) 24  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) 25  Geodesic diffusion  123

26. Method.B (8/8) 26  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) 27

28. Experimental Results (2/8) 28

29. Experimental Results (3/8) 29

30. Experimental Results (4/8) 30

31. Experimental Results (5/) 31

32. Experimental Results (6/8) 32

33. Experimental Results (7/8) 33

34. Experimental Results (8/8) 34

35. CUDA 35  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 36