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Stereo Using Monocular Cues within the Tensor Voting Framework
June 2006 (vol. 28 no. 6)
pp. 968-982
ASCII Text
x 
 
Philippos Mordohai, G?rard Medioni, "Stereo Using Monocular Cues within the Tensor Voting Framework," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 6, pp. 968-982, June, 2006.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2006.129,
author = {Philippos Mordohai and G?rard Medioni},
title = {Stereo Using Monocular Cues within the Tensor Voting Framework},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {28},
number = {6},
issn = {0162-8828},
year = {2006},
pages = {968-982},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.129},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Stereo Using Monocular Cues within the Tensor Voting Framework
IS - 6
SN - 0162-8828
SP968
EP982
EPD - 968-982
A1 - Philippos Mordohai,
A1 - G?rard Medioni,
PY - 2006
KW - Stereo
KW - occlusion
KW - pixel correspondence
KW - computer vision
KW - perceptual organization
KW - tensor voting.
VL - 28
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
We address the fundamental problem of matching in two static images. The remaining challenges are related to occlusion and lack of texture. Our approach addresses these difficulties within a perceptual organization framework, considering both binocular and monocular cues. Initially, matching candidates for all pixels are generated by a combination of matching techniques. The matching candidates are then embedded in disparity space, where perceptual organization takes place in 3D neighborhoods and, thus, does not suffer from problems associated with scanline or image neighborhoods. The assumption is that correct matches produce salient, coherent surfaces, while wrong ones do not. Matching candidates that are consistent with the surfaces are kept and grouped into smooth layers. Thus, we achieve surface segmentation based on geometric and not photometric properties. Surface overextensions, which are due to occlusion, can be corrected by removing matches whose projections are not consistent in color with their neighbors of the same surface in both images. Finally, the projections of the refined surfaces on both images are used to obtain disparity hypotheses for unmatched pixels. The final disparities are selected after a second tensor voting stage, during which information is propagated from more reliable pixels to less reliable ones. We present results on widely used benchmark stereo pairs.

[1] G. Medioni, M.S. Lee, and C.K. Tang, A Computational Framework for Segmentation and Grouping. Elsevier, 2000.
[2] R. Sara, “Finding the Largest Unambiguous Component of Stereo Matching,” Proc. European Conf. Computer Vision, vol. 3, pp. 900-914, 2002.
[3] M.S. Lee and G. Medioni, “Inferring Segmented Surface Description from Stereo Data,” Proc. Int'l Conf. Computer Vision and Pattern Recognition, pp. 346-352, 1998.
[4] M.S. Lee, G. Medioni, and P. Mordohai, “Inference of Segmented Overlapping Surfaces from Binocular Stereo,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 6, pp. 824-837, June 2002.
[5] P. Mordohai and G. Medioni, “Stereo Using Monocular Cues within the Tensor Voting Framework,” Proc. European Conf. Computer Vision, pp. 588-601, 2004.
[6] W. Hoff and N. Ahuja, “Surfaces from Stereo: Integrating Feature Matching, Disparity Estimation, and Contour Detection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, no. 2, pp. 121-136, Feb. 1989.
[7] D. Scharstein and R. Szeliski, “A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms,” Int'l J. Computer Vision, vol. 47, nos. 1-3, pp. 7-42, Apr. 2002.
[8] M.Z. Brown, D. Burschka, and G.D. Hager, “Advances in Computational Stereo,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 8, pp. 993-1008, Aug. 2003.
[9] T. Kanade and M. Okutomi, “A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 9, pp. 920-932, Sept. 1994.
[10] O. Veksler, “Fast Variable Window for Stereo Correspondence Using Integral Images,” Proc. Int'l Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 556-561, 2003.
[11] S. Birchfield and C. Tomasi, “A Pixel Dissimilarity Measure that Is Insensitive to Image Sampling,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 4, pp. 401-406, Apr. 1998.
[12] R. Szeliski and D. Scharstein, “Symmetric Subpixel Stereo Matching,” Proc. European Conf. Computer Vision, vol. 2, pp. 525-540, 2002.
[13] M. Agrawal and L.S. Davis, “Window-Based, Discontinuity Preserving Stereo,” Proc. Int'l Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 66-73, 2004.
[14] Y. Ohta and T. Kanade, “Stereo by Intra- and Interscanline Search Using Dynamic Programming,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 7, no. 2, pp. 139-154, Mar. 1985.
[15] P.N. Belhumeur and D. Mumford, “A Bayesian Treatment of the Stereo Correspondence Problem Using Half-Occluded Regions,” Proc. Int'l Conf. Computer Vision and Pattern Recognition, pp. 506-512, 1992.
[16] S.S. Intille and A.F. Bobick, “Disparity-Space Images and Large Occlusion Stereo,” Proc. European Conf. Computer Vision, vol. 2, pp. 179-186, 1994.
[17] D. Geiger, B. Ladendorf, and A. Yuille, “Occlusions and Binocular Stereo,” Int'l J. Computer Vision, vol. 14, no. 3, pp. 211-226, Apr. 1995.
[18] P.N. Belhumeur, “A Bayesian-Approach to Binocular Stereopsis,” Int'l J. Computer Vision, vol. 19, no. 3, pp. 237-260, Aug. 1996.
[19] S. Birchfield and C. Tomasi, “Depth Discontinuities by Pixel-to-Pixel Stereo,” Proc. Int'l Conf. Computer Vision, pp. 1073-1080, 1998.
[20] A.F. Bobick and S.S. Intille, “Large Occlusion Stereo,” Int'l J. Computer Vision, vol. 33, no. 3, pp. 1-20, Sept. 1999.
[21] I.J. Cox, S.L. Hingorani, S.B. Rao, and B.M. Maggs, “A Maximum-Likelihood Stereo Algorithm,” Computer Vision and Image Understanding, vol. 63, no. 3, pp. 542-567, May 1996.
[22] S. Roy and I.J. Cox, “A Maximum-Flow Formulation of the N-Camera Stereo Correspondence Problem,” Proc. Int'l Conf. Computer Vision, pp. 492-499, 1998.
[23] H. Ishikawa and D. Geiger, “Occlusions, Discontinuities, and Epipolar Lines in Stereo,” Proc. European Conf. Computer Vision, vol. 1, pp. 232-248, 1998.
[24] V. Kolmogorov and R. Zabih, “Computing Visual Correspondence with Occlusions via Graph Cuts,” Proc. Int'l Conf. Computer Vision, vol. 2, pp. 508-515, 2001.
[25] Y. Boykov, O. Veksler, and R. Zabih, “Fast Approximate Energy Minimization via Graph Cuts,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 11, pp. 1222-1239, Nov. 2001.
[26] V. Kolmogorov and R. Zabih, “Multi-Camera Scene Reconstruction via Graph Cuts,” Proc. European Conf. Computer Vision, vol. 3, pp. 82-96, 2002.
[27] D. Marr and T.A. Poggio, “Cooperative Computation of Stereo Disparity,” Science, vol. 194, no. 4262, pp. 283-287, Oct. 1976.
[28] C.L. Zitnick and T. Kanade, “A Cooperative Algorithm for Stereo Matching and Occlusion Detection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 7, pp. 675-684, July 2000.
[29] A. Luo and H. Burkhardt, “An Intensity-Based Cooperative Bidirectional Stereo Matching with Simultaneous Detection of Discontinuities and Occlusions,” Int'l J. Computer Vision, vol. 15, no. 3, pp. 171-188, July 1995.
[30] Y. Zhang and C. Kambhamettu, “Stereo Matching with Segmentation-Based Cooperation,” Proc. European Conf. Computer Vision, vol. 2, pp. 556-571, 2002.
[31] D. Scharstein and R. Szeliski, “Stereo Matching with Nonlinear Diffusion,” Int'l J. Computer Vision, vol. 28, no. 2, pp. 155-174, 1998.
[32] J. Sun, N.N. Zheng, and H.Y. Shum, “Stereo Matching Using Belief Propagation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 7, pp. 787-800, July 2003.
[33] J. Sun, Y. Li, S.B. Kang, and H.Y. Shum, “Symmetric Stereo Matching for Occlusion Handling,” Proc. Int'l Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 399-406, 2005.
[34] Z. Zhang and Y. Shan, “A Progressive Scheme for Stereo Matching,” Lecture Notes in Computer Science, vol. 2018, pp. 68-85, 2001.
[35] J.Y. Goulermas and P. Liatsis, “A Collective-Based Adaptive Symbiotic Model for Surface Reconstruction in Area-Based Stereo,” IEEE Trans. Evolutionary Computation, vol. 7, no. 5, pp. 482-502, 2003.
[36] S. Birchfield and C. Tomasi, “Multiway Cut for Stereo and Motion with Slanted Surfaces,” Proc. Int'l Conf. Computer Vision, pp. 489-495, 1999.
[37] H. Tao, H.S. Sawhney, and R. Kumar, “A Global Matching Framework for Stereo Computation,” Proc. Int'l Conf. Computer Vision, vol. 1, pp. 532-539, 2001.
[38] M.H. Lin and C. Tomasi, “Surfaces with Occlusions from Layered Stereo,” Proc. Int'l Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 710-717, 2003.
[39] Y. Wei and L. Quan, “Region-Based Progressive Stereo Matching,” Proc. Int'l Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 106-113, 2004.
[40] L. Hong and G. Chen, “Segment-Based Stereo Matching Using Graph Cuts,” Proc. Int'l Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 74-81, 2004.
[41] M.F. Tappen and W.T. Freeman, “Comparison of Graph Cuts with Belief Propagation for Stereo, Using Identical MRF Parameters,” Proc. Int'l Conf. Computer Vision, pp. 900-907, 2003.
[42] A.S. Ogale and Y. Aloimonos, “Stereo Correspondence with Slanted Surfaces: Critical Implications of Horizontal Slant,” Proc. Int'l Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 568-573, 2004.
[43] M. Okutomi, Y. Katayama, and S. Oka, “A Simple Stereo Algorithm to Recover Precise Object Boundaries and Smooth Surfaces,” Int'l J. Computer Vision, vol. 47, nos. 1-3, pp. 261-273, Apr. 2002.
[44] R. Sara and R. Bajcsy, “On Occluding Contour Artifacts in Stereo Vision,” Proc. Int'l Conf. Computer Vision and Pattern Recognition, pp. 852-857, 1997.
[45] C.K. Tang and G. Medioni, “Inference of Integrated Surface, Curve, and Junction Descriptions from Sparse 3D Data,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 11, pp. 1206-1223, Nov. 1998.
[46] D. Scharstein and R. Szeliski, “High-Accuracy Stereo Depth Maps Using Structured Light,” Proc. Int'l Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 195-202, 2003.

Index Terms:
Stereo, occlusion, pixel correspondence, computer vision, perceptual organization, tensor voting.
Citation:
Philippos Mordohai, G?rard Medioni, "Stereo Using Monocular Cues within the Tensor Voting Framework," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 6, pp. 968-982, June 2006, doi:10.1109/TPAMI.2006.129
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