loading...
 This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Sampling the Disparity Space Image
March 2004 (vol. 26 no. 3)
pp. 419-425

Abstract—A central issue in stereo algorithm design is the choice of matching cost. Many algorithms simply use squared or absolute intensity differences based on integer disparity steps. In this paper, we address potential problems with such approaches. We begin with a careful analysis of the properties of the continuous disparity space image (DSI) and propose several new matching cost variants based on symmetrically matching interpolated image signals. Using stereo images with ground truth, we empirically evaluate the performance of the different cost variants and show that proper sampling can yield improved matching performance.

[1] 419 P. Anandan, A Computational Framework and an Algorithm for the Measurement of Visual Motion Int'l J. Computer Vision, vol. 2, no. 3, pp. 283-310, 1989.[2] P.N. Belhumeur, A Bayesian Approach to Binocular Stereopsis Int'l J. Computer Vision, vol. 19, no. 3, pp. 237-260, 1996.[3] 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.[4] S. Birchfield and C. Tomasi, Multiway Cut for Stereo and Motion with Slanted Surfaces Proc. Int'l Conf. Computer Vision, pp. 489-495, 1999.[5] A.F. Bobick and S.S. Intille, Large Occlusion Stereo Int'l J. Computer Vision, vol. 33, no. 3, pp. 181-200, 1999.[6] 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.[7] K.N. Kutulakos, Approximate N-View Stereo Proc. European Conf. Computer Vision, vol. I, pp. 67-83, 2000.[8] C. Loop and Z. Zhang, “Computing Rectifying Homographies for Stereo Vision,” Proc. Computer Vision and Pattern Recognition '99, vol. I, pp. 125-131, 1999.[9] B.D. Lucas and T. Kanade, An Iterative Image Registration Technique with an Application in Stereo Vision Proc. Int'l Joint Conf. Artificial Intelligence, pp. 674-679, 1981.[10] L.H. Matthies, R. Szeliski, and T. Kanade, Kalman Filter-Based Algorithms for Estimating Depth from Image Sequences Int'l J. Computer Vision, vol. 3, pp. 209-236, 1989.[11] M. Okutomi and T. Kanade, “A Multiple-Baseline Stereo,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 4, pp. 353-363, Apr. 1993.[12] D. Scharstein and R. Szeliski, Stereo Matching with Nonlinear Diffusion Int'l J. Computer Vision, vol. 28, no. 2, pp. 155-174, 1998.[13] D. Scharstein and R. Szeliski, A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms Int'l J. Computer Vision, vol. 47, no. 1, pp. 7-42, 2002.[14] M. Shimizu and M. Okutomi, Precise Sub-Pixel Estimation on Area-Based Matching Int'l Conf. Computer Vision, vol. I, pp. 90-97, 2001.[15] R. Szeliski and M.R. Ito, New Hermite Cubic Interpolator for Two-Dimensional Curve Generation IEE Proc. E, vol. 133, no. 6, pp. 341-347, 1986.[16] R. Szeliski and D. Scharstein, Symmetric Sub-Pixel Stereo Matching Proc. European Conf. Computer Vision, vol. II, pp. 525-540, 2002.[17] H. Tao, H. Sawhney, and R. Kumar, A Global Matching Framework for Stereo Computation Proc. Int'l Conf. Computer Vision, pp. 532-539, 2001.[18] Q. Tian and M.N. Huhns, Algorithms for Subpixel Registration Computer Vision, Graphics, and Image Processing, vol. 35, pp. 220-233, 1986.[19] Y. Tsin, V. Ramesh, and T. Kanade, Statistical Calibration of the CCD Imaging Process Proc. Int'l Conf. Computer Vision, vol. 1, pp. 480-487, 2001.[20] Y. Yang, A. Yuille, and J. Lu, Local, Global, and Multilevel Stereo Matching Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 274-279, 1993.

Index Terms:
Stereo algorithms, matching cost, subpixel sampling, disparity space image, aliasing.
Citation:
Richard Szeliski, Daniel Scharstein, "Sampling the Disparity Space Image," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 3, pp. 419-425, Mar. 2004, doi:10.1109/TPAMI.2004.1262341
Usage of this product signifies your acceptance of the Terms of Use.