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Stereo Matching

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

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Stereo Matching

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  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

  2. Outline • Introduction • Related Works • Methods • Conclusion

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

  4. Related Works • StereoMatching • Thesameobject,thesamedisparity • Segmentation • Calculatecorrespondpixelssimilarity(colorandgeographic distance) • Occlusionhandling • Refinement

  5. Related Works • GlobalMethods • Energy minimization process (GC,BP,DP,Cooperative) • Per-processing • Accuratebutslow • LocalMethods • A local support region with winner take all • Fastbutinaccurate. • AdaptiveSupportWeight

  6. RelatedWorks • Local methodsalgorithm [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. Related Works • Edge Preserving filter:Remove noise and preserve structure/edge,likeobjectconsideration. • AdaptiveSupportWeight[3] • Bilateral filter(BF) [34] • Guided filter(GF)[5] • Geodesicdiffusion[33] • ArbitrarySupportRegion [39]

  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.

  9. Information Permeability For Stereo Matching MethodA.

  10. Methods A. • Goal : Gethighqualitybutlowcomplexity Savememory Real-timeapplication • SuccessiveWeightedSummation(SWS) • Constanttimefiltering+Weightedaggregation ◎Qingqing Yang, Dongxiao Li, Lianghao Wang, and Ming Zhang, “Full-Image Guided Filtering for Fast Stereo Matching”, Signal Processing Letters, IEEEMarch 2013 http://www.camdemy.com/media/7110

  11. Methods A. • Cost Computation

  12. Census Transform Census transform window :

  13. Census Hamming Distance • Left image • Right image Hamming Distance = 3 XOR

  14. Methods A. • Cost Computation

  15. Methods A. Cost Aggregation

  16. Methods A. • Cost Aggregation

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

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

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

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

  21. Disparity Variation

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

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

  24. Experimental Results A. • Device : Core Duo 1.80 GHz 2G Ram CPU • Implemented in C++ • Parameter : (T, α,)=(15, 0.2, 8)

  25. Parameter of Method A.

  26. Experimental Results A.

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

  28. Experimental Results A. • 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.

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

  30. Experimental Results A.

  31. Computational times A.

  32. Error Analysis A.

  33. Comparison with Full-Image◎ ◎Qingqing Yang, Dongxiao Li, Lianghao Wang, and Ming Zhang, “Full-Image Guided Filtering for Fast Stereo Matching”, Signal Processing Letters, IEEE

  34. Comparison with Full-Image

  35. Full-Image Results

  36. Ground Truth Proposed Results Full-Image Results

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

  38. Radiometric Invariant Stereo Matching Based On Relative Gradients MethodB.

  39. Methods B. • Goal : Adaptdifferentenvironmentalfactors.(Illuminationcondition) Effectiveandrobustalgorithm • Relativegradientalgorithm+Gaussianweightedfunction

  40. Background • LightingModel: • Viewindependent,bodyreflection

  41. Background • LightingModel: ANCC

  42. Method B. • Cost Computation

  43. Method B. • Cost Aggregation • Refinement • AvoidWhiteandblacknoises

  44. Experimental Results B.

  45. Experimental Results B.

  46. Experimental Results B.

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

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