Abstract
Two central issues in stereo algorithm design are the matching criterion and the underlying smoothness assumptions. In this paper we propose a new stereo algorithm with novel approaches to both issues. We start with a careful analysis of the properties of the continuous disparity space image (DSI), and derive a new matching cost based on the reconstructed image signals. We then use a symmetric matching process that employs visibility constraints to assign disparities to a large fraction of pixels with minimal smoothness assumptions. While the matching operates on integer disparities, sub-pixel information is maintained throughout the process. Global smoothness assumptions are delayed until a later stage in which disparities are assigned in textureless and occluded areas. We validate our approach with experimental results on stereo images with ground truth.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
P. Anandan. A computational framework and an algorithm for the measurement of visual motion. International Journal of Computer Vision, 2(3):283–310, January 1989.
P. N. Belhumeur. A Bayesian approach to binocular stereopsis. International Journal of Computer Vision, 19(3):237–260, August 1996.
S. Birchfield and C. Tomasi. A pixel dissimilarity measure that is insensitive to image sampling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(4):401–406, April 1998.
S. Birchfield and C. Tomasi. Multiway cut for stereo and motion with slanted surfaces. In Seventh International Conference on Computer Vision (ICCV’99), pages 489–495, Kerkyra, Greece, September 1999.
A. F. Bobick and S. S. Intille. Large occlusion stereo. International Journal of Computer Vision, 33(3): 181–200, September 1999.
Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11):1222–1239, November 2001.
P. Fua. A parallel stereo algorithm that produces dense depth maps and preserves image features. Machine Vision and Applications, 6(1):35–49, Winter 1993.
T. Kanade and M. Okutomi. A stereo matching algorithm with an adaptive window: Theory and experiment. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(9):920–932, September 1994.
C. Loop and Z. Zhang. Computing rectifying homographies for stereo vision. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’ 99), volume I, pages 125–131, Fort Collins, June 1999.
B. D. Lucas and T. Kanade. An iterative image registration technique with an application in stereo vision. In Seventh International Joint Conference on Artificial Intelligence (IJCAI-81), pages 674–679, Vancouver, 1981.
L. H. Matthies, R. Szeliski, and T. Kanade. Kalman filter-based algorithms for estimating depth from image sequences. International Journal of Computer Vision, 3:209–236, 1989.
M. Okutomi and T. Kanade. A multiple baseline stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(4):353–363, April 1993.
D. Scharstein and R. Szeliski. Stereo matching with nonlinear diffusion. International Journal of Computer Vision, 28(2):155–174, July 1998.
D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47(1):7–42, May 2002.
M. Shimizu and M. Okutomi. Precise sub-pixel estimation on area-based matching. In Eighth International Conference on Computer Vision (ICCV 2001), volume I, pages 90–97, Vancouver, Canada, July 2001.
H. Tao, H.S. Sawhney, and R. Kumar. A global matching framework for stereo computation. In Eighth International Conference on Computer Vision (ICCV 2001), volume I, pages 532–539, Vancouver, Canada, July 2001.
Q. Tian and M. N. Huhns. Algorithms for subpixel registration. Computer Vision, Graphics, and Image Processing, 35:220–233, 1986.
Y. Tsin, V. Ramesh, and T. Kanade. Statistical calibration of CCD imaging process. In Eighth International Conference on Computer Vision (ICCV 2001), volume I, pages 480–487, Vancouver, Canada, July 2001.
Y. Yang, A. Yuille, and J. Lu. Local, global, and multilevel stereo matching. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’ 93), pages 274–279, New York, New York, June 1993. IEEE Computer Society.
Z. Zhang and Y. Shan. A progressive scheme for stereo matching. In M. Pollefeys et al., editors, Second European Workshop on 3D Structure from Multiple Images of Large-Scale Environments (SMILE 2000), pages 68–85, Dublin, Ireland, July 2000.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Szeliski, R., Scharstein, D. (2002). Symmetric Sub-pixel Stereo Matching. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds) Computer Vision — ECCV 2002. ECCV 2002. Lecture Notes in Computer Science, vol 2351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47967-8_35
Download citation
DOI: https://doi.org/10.1007/3-540-47967-8_35
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43744-4
Online ISBN: 978-3-540-47967-3
eBook Packages: Springer Book Archive