1. Stereo Matching Information Permeability For Stereo Matching – Cevahir Cigla and A.Aydın Alatan – Signal Processing: Image Communication, 2013 Radiometric Invariant Stereo Matching Based On Relative Gradients – Xiaozhou Zhou and Pierre Boulanger – International Conference on Image Processing (ICIP), IEEE 2012 1

2. Outline Introduction Related Works Methods Conclusion 2

3. Introduction Goal – Get accurate disaprity maps effectively. – Find more robust algorithm, especially refinement technique. Foucus : Refinement step and Comparison 3

4. Related Works Stereo Matching – The same object, the same disparity Segmentation Calculate correspond pixels similarity (color and geographic distance) – Occlusion handling Refinement 4

5. Related Works Global Methods – Energy minimization process (GC,BP,DP,Cooperative) – Per-processing – Accurate but slow Local Methods – A local support region with winner take all – Fast but inaccurate. – Adaptive Support Weight 5

6. Related Works Disparity Refinement Disparity Optimization Cost Aggregation Matching Cost Computation 6 Local methods algorithm [1] D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision (IJCV), 47:7–42, 2002.

7. Edge Preserving filter : Remove noise and preserve structure/edge, like object consideration. Adaptive Support Weight [3] Bilateral filter(BF) [34] Guided filter(GF) [5] Geodesic diffusion [33] Arbitrary Support Region [39] Related Works 7

8. Reference Papers [3] Kuk-JinYoon, InSoKweon, Adaptive support weight approach for correspondence search, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006. [5] C. Rhemann, A. Hosni, M. Bleyer, C. Rother, M. Gelautz, Fast cost-volume filtering for visual correspondence and beyond, CVPR 2011. [33] L. De-Maetzu, A. Villanueva nad, R. Cabeza, Near real-time stereo matching using geodesic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012. [34] A. Ansar, A. Castano, L. Matthies, Enhanced real time stereo using bilateral filtering, in: Proceedings of the International Symposium on 3D Data Processing Visualization and Transmission, 2004. [39] X. Mei, X Sun, M Zhou, S. Jiao, H. Wang, Z. Zhang, On building an accurate stereo matching system on graphics hardware, in: Proceed- ings of GPUCV 2011. 8

9. Information Permeability For Stereo Matching Method A. 9

10. Methods A. Goal : Get high quality but low complexity Save memory Real-time application Successive Weighted Summation (SWS) – Constant time filtering + Weighted aggregation 10 ◎ Qingqing Yang, Dongxiao Li, Lianghao Wang, and Ming Zhang, “Full-Image Guided Filtering for Fast Stereo Matching”, Signal Processing Letters, IEEE March 2013 http://www.camdemy.com/media/7110

11. Methods A. Cost Computation 11

12. Census Transform 11000 11000 11X00 00011 11111 121130263139 109115334030 98102786745 476732170198 398699159210 110001100011000001111111 Census transform window :

13. Census Hamming Distance Left image Right image Hamming Distance = 3 110001100011000001111111 111001100101000001111111 XOR 001000000110000000000000

14. Methods A. Cost Computation 14

15. Methods A. 15 Cost Aggregation

16. Methods A. Cost Aggregation 16

17. Methods A. 17 (b)Horizontal effective weights (c)Vertical effective weights(d)2D effective weights

18. 18 (a) AW [3] (b) Geodesic support [12] (c) Arbitrary support region [4] (d) Proposed Comparison With Other Methods

19. Methods A. Refinement – Using cross-check to detect reliable and occluded region detection 19 ф is a constant (set to 0.1 throughout experiments)

20. Methods A. 20 (a)Linear mapping function for reliable pixels based on disparities (b)The resultant map for the left image

21. Disparity Variation 21 Before  After 0 1.15 1 1.30 2 1.45 3 1.60 4 1.75 5 1.90 6 2.05 7 2.20 8 2.35 9 2.50 10 2.65 11 2.80 12 2.95 13 3.10 14 3.25 15 3.40 16 3.55 17 3.70 18 3.85 19 4 20 4.15 21 4.30 22 4.45 23 4.60 24 4.75 25 4.90 26 5.05 27 5.20 28 5.35 29 5.50 30 5.65 31 5.80 32 5.95 33 6.10 34 6.25 35 6.40 36 6.55 37 6.70 38 6.85 39 7 40 7.15 41 7.30 42 7.45 43 7.60 44 7.75 45 7.90 46 8.05 47 8.20 48 8.35 49 8.50 50 8.65 51 8.80 52 8.95 53 9.10 54 9.25 55 9.40 56 9.55 57 9.70 58 9.85 59 10

22. 22 (b) Without occlusion handling, bright regions correspond to small disparities (c) Detection of occluded and un-reliable regions

23. Methods A. 23 (b) occlusion handling with no background favoring (c) the proposed occlusion handling

24. Experimental Results A. 24

25. 25

26. Parameter of Method A. 26

27. 27

28. Experimental Results A. 28

29. Experimental Results A. 29 6D + 4D * V.S. 129D + 21D * 10~15X

30. Experimental Results A. 30 Proposed method is the fastest method without any special hardware implementation among Top-10 local methods of the Middlebury test bench, as of February 2013.

31. 31 Proposed O(1) AW Guided filter Geodesic support Arbitrary shaped cross filter

32. Experimental Results A. 32

33. Computational times A. 33

34. Error Analysis A. 34

35. Comparison with Full-Image ◎ Full-ImageProposed InitializationAD + GradientSAD + Census Aggregation Refinement1.Cross checking (lowest disparity) 2.Weighted median filter 1. Cross checking (normalized disparity) 2. Median filter (background handling) 35 ◎ Qingqing Yang, Dongxiao Li, Lianghao Wang, and Ming Zhang, “Full-Image Guided Filtering for Fast Stereo Matching”, Signal Processing Letters, IEEE

36. Comparison with Full-Image 36

37. 37 Full-Image Results

38. 38 Full-Image Results Proposed Results Ground Truth

39. Comparison with Full-Image My Experimental Results (SAD+Gradient) Lowest V.S. Normalized disparity 39

40. Radiometric Invariant Stereo Matching Based On Relative Gradients Method B. 40

41. Methods B. Goal : Adapt different environmental factors.(Illumination condition) Effective and robust algorithm Relative gradient algorithm + Gaussian weighted function 41

42. Background Lighting Model : – View independent, body reflection 42

43. Background Lighting Model : 43 ANCC

44. Method B. Cost Computation – 44 (i,j)

45. Method B. Cost Aggregation – Refinement – – Avoid White and black noises 45

46. Experimental Results B. 46

47. 47

48. Experimental Results B. 48

49. Experimental Results B. 49

50. Experimental Results B. My Experimental Results (SAD+Gradient) Original V.S.Rerange disparity 50

51. Experimental Results B. Using related gradient intialization 51

52. Conclusion Initialization ADc/SADcADgC-CensusG-Census??? Aggregation Weighted-WindowPermeabilityCost-Filter Arbitrary Support Region ??? Refinement Lowest NeighborNormalizesRe-RangeScan-line??? 52