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Window-based Approach For Fast Stereo Correspondence

Window-based Approach For Fast Stereo Correspondence. Raj Kumar Gupta, Siu-Yeung Cho IET Computer Vision,   2013. Outline. Introduction Related Work Proposed Method Experimental Results Conclusion. Introduction.

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Window-based Approach For Fast Stereo Correspondence

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  1. Window-based Approach For Fast Stereo Correspondence Raj Kumar Gupta, Siu-Yeung Cho IET Computer Vision,  2013

  2. Outline • Introduction • Related Work • ProposedMethod • ExperimentalResults • Conclusion

  3. Introduction • Using two correlation windows to improve the performance of the algorithm • 3*3 and 9*9 • Real-time suitability • more than 10 frame/s on CPU in case of 320 × 240-sized image pair with disparity value 16

  4. Related Work • Local methods are usually base on correlation. • Area-based (NCC, SAD, SSD) • Feature-based: rely on feature extraction and match local cues (BF, GF) • Bigger window size, more information, more blurred.

  5. Outline • Introduction • Related Work • ProposedMethod • ExperimentalResults • Conclusion

  6. Flow Chart

  7. Flow Chart

  8. Initial Matching • Matching cost computation: SAD d Right Left

  9. Problem in disparity selection a. Determine disparity easily for unique minimum value b. Ambiguous disparity in case of multiple minima c. Matching cost calculated at point (205, 230) of Tsukuba image

  10. Initial Matching: large correlation window • Matching cost computation: SAD + penalty • Penalty term • Disparity computation

  11. Problem in disparity selection

  12. Initial Matching: small correlation window • Only those disparity values that are carried by neighbouring pixels. • Matching cost computation without penalty • N: the disparity values of the neighbouring pixels. • Avoid local minima and speed up

  13. Flow Chart

  14. Unreliable pixel detection • left–right cross-checking

  15. Disparity Interpolation • Search for pixels with reliable disparity value in its eight neighbouringpixels. • Compute similarity of unreliable pixel and its reliable neighbor.

  16. Flow Chart

  17. Disparity Refinement

  18. Outline • Introduction • Related Work • ProposedMethod • ExperimentalResults • Conclusion

  19. ExperimentalResults • Computation time of the proposed algorithm for different window sizes on Tsukuba image.(image size 384 × 288 with 16 disparity labels)

  20. Percentage error in non-occluded (nocc), whole image (all) and near depth discontinuities (disc) for different window sizes for all four images (Tsukuba, Venus, Teddy and Cones)

  21. ExperimentalResults • a. Without using small correlation window Ws and the disparity refinement step • b. Without using the disparity refinement step • c. Without using small correlation window Ws • d. With all four steps on Tsukuba image

  22. ExperimentalResults

  23. ExperimentalResults • Comparison the performance of the proposed algorithm with other correlation-based algorithms.

  24. ExperimentalResults

  25. Reference • [24] Gupta, R., Cho, S.-Y.: ‘Real-time stereo matching using adaptive binary window’ (3D Data Processing, Visualization and Transmission, 2010) • [25] Zhang, K., Lu, J., Lafruit, G., Lauwereins, R., Gool, L.V.: ‘Real-time accurate stereo with bitwise fast voting on Cuda’. Int. Conf. Computer Vision Workshops, 2009, pp. 540–547 • [26] Humenberger, M., Zinner, C., Weber, M., Kubinger, W., Vincze, M.:‘A fast stereo matching algorithm suitable for embedded real-time systems’, Comput. Vis. Image Underst., 2010, 114, (11),pp. 1180–1202 • [27] Gong, M., Yang, Y.: ‘Near real-time reliable stereo matching using programmable graphics hardware’. IEEE Conf. Computer Vision and Pattern Recognition, 2005, pp. 924–931 • [28] Richardt, C., Orr, D., Davies, I., Criminisi, A., Dodgson, N.: ‘Real-time spatiotemporal stereo matching using the dual-cross-bilateral grid’. European Conf. Computer Vision, 2010, vol. 6313, pp. 510–523 • [29] Ambrosch, K., Kubinger, W.: ‘Accurate hardware-based stereo vision’,Comput. Vis. Image Underst., 2010, 114, (11), pp. 1303–1316

  26. Conclusion • A new correlation-based stereo-matching approach. • Large window improves at non-textured image regions • Small window improves at depth discontinuities • The CPU implementation computes at a speed of more than 10 frame/s. • Easily implemented on GPU. • The proposed method can be used in real-time applications to reconstruct the 3D structures with great accuracy at object boundaries.

  27. Codebook based Stereo Matching for Natural User Interface Sung-il Kang and Hyunki Hong 2013 IEEE International Conference on Consumer Electronics (ICCE)

  28. Outline • Introduction • ProposedMethod • ExperimentalResults • Conclusion

  29. Introduction • Interactive user interface has been one of the major topics in consumer electronics. • Gesture based user interface • Interactive smart TV, Nintendo Wii, Sony PlayStation3 Move, and Microsoft Kinect. • Propose a stereo system implemented on GPGPU for real-time performance. • Employcodebooktosolveocclusion.

  30. Flow chart

  31. Proposed Method • Pre-processing • Laplace od Gaussian (LoG) filter for alleviating the lighting effects. • Cost initialization • AD+Census[6] [6] X. Mei, X. Sun, M. Zhou, H. Wang, and X. Zhang, “On building an accurate stereo matchng system on graphics hardware,” Proc. of GPUCV, pp. 467-474, 2011. http://www.camdemy.com/media/4724

  32. Proposed Method • Cost aggregation[6] • Cross-based aggregation • Color similarity and the length constraint • Refinement[6,7] • Left-right consistency check • Iterative region voting • Sub-pixel enhancement d [7] Q. Yang, C. Engels, R. Yang, H. Stewenius, and D. Nister, “Stereo matching with color-weighted correlation, hierarchical belief propagation and occlusion handling,” IEEE Transactions on PAMI, 2009.

  33. Proposed Method Yes Occlusion? Find codeword No Yes Codeword? Update codeword No Add a new codeword

  34. ExperimentalResults • Device: Intel Quad 2.66GHz with Nvidia GTX460. • Stereo images are captured by a Bumblebee 3 from Point Grey Inc. • Time: 80~110ms/frame • Stereo matching is implemented on GPU. • The codebook generation and its evaluation is on CPU.

  35. ExperimentalResults

  36. ExperimentalResults [8] K. J. Yoon and I. S. Kweon, “Adaptive support-weight approach for correspondence search,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, pp. 650-656, 2005. [9] C. Richardt, D Orr, I Davies, and A Criminisi, “Real-time spatiotemporal stereo matching using the dual-cross-bilateral grid,” Proc. of ECCV, 2010.

  37. Conclusion • Propose a stereo system implemented on GPGPU for real-time performance. • Good performance at static background Only.

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