Abstract
Stereo computation is just one of the vision problems where the presence of outliers cannot be neglected. Most standard algorithms make unrealistic assumptions about noise distributions, which leads to erroneous results that cannot be corrected in subsequent processing stages. In this work the standard area-based correlation approach is modified so that it can tolerate a significant number of outliers. The approach exhibits a robust behaviour not only in the presence of mismatches but also in the case of depth discontinuities. Experimental results are given on synthetic and real images.
This work was supported by a grant from the Austrian National Fonds zur Förderung der wissenschaftlichen Forschung (No. S7002MAT) and (No. P9110-SPR).
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P. J. Besl, J. B. Birch, and L. T. Watson. Robust window operators. In Proceedings of the 2nd ICCV, pages 591–600. IEEE, Dec 1988.
D.N. Bhat and S.K. Nayar. Ordinal measures for visual correspondence. Technical Report CUCS-009-96, Columbia University,Dept. of Computer Science, February 1996.
H. Helmke, R. Janssen, and G. Saur. Automatische Erzeugung dreidimensionaler Kantenmodelle aus mehreren zweidimensionalen Objektansichten. In Grabkopf R.E., editor, Mustererkennung 1990, 12. DAGM Symposium, pages 617–624, 1990.
P. J. Huber. Robust Statistics. Wiley, New York, 1981.
M.R.M. Jenkin, A.D. Jepson, and J.K. Tsotsos. Techniques for disparity measurements. CVGIP, 53:14–30, 1991.
D. Y. Kim, J. J. Kim, P. Meer, D. Mintz, and A. Rosenfeld. Robust computer vision: A least-median of squares based approach. In Proceedings of the Image Understanding Workshop, pages 1117–1134, Palo Alto, CA, May 1989. DARPA.
W. Luo and H. Maitre. Using surface model to correct and fit disparity data in stereo vision. In Tenth International Conference on Pattern Recognition, pages 60–64, Atlantic City, NJ, June 16–21 1990.
D. Marr and T. Poggio. A computational theory of human stereo vision. Proc. R. Soc. Lond. B., 204:301–328, 1979.
P. Meer, D. Mintz, A. Rosenfeld, and D. Y. Kim. Robust regression methods for computer vision: A review. International Journal of Computer Vision, 6(1):59–70, 1991.
C. Menard. Robust Stereo and Adaptive Matching in Correlation Scale-Space. PhD thesis, TU Wien, Institut für Automation, PRIP, Wien, 1996.
Azriel Rosenfeld and Avinash C. Kak. Digital Picture Processing Volume 2. Academic Press, Inc., 1982.
P. J. Rousseuw and A. M. Leroy. Robust Regression and Outlier Detection. Wiley, New York, 1987.
B. G. Schunck. Robust computational vision. In Proc. Of the IWRCV, Seattle, WA, Oct 1990.
S. S. Sinha and B. G. Schuck. A two-stage algorithm for discontinuity-preserving surface reconstruction. IEEE Trans. on PAMI, 14(1):36–55, Jan 1992.
J. Subrahmonia, J. Hung, and D.B. Cooper. Model-based segmentation and estimation of 3d surfaces from two or more intensity images using markov random fields. IEEE Conf. on Pattern Recognition, 1:390–397, 1990.
D. Terzopoulos, A. Witkin, and M. Kass. Stereo matching as constrained optimization using scale continuation methods. In Opt. Dig. PR/ SPIE 754, pages 92–99, 1987.
J.W. Tukey. Some advanced thoughts on the data analysis involved in configural polysampling directed toward high performance estimates. Technical Report 189, Series 2, Department of Statistics, Princeton University, Princeton,N.J., 1981.
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© 1997 Springer-Verlag Berlin Heidelberg
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Menard, C., Leonardis, A. (1997). Stereo matching using M-estimators. In: Sommer, G., Daniilidis, K., Pauli, J. (eds) Computer Analysis of Images and Patterns. CAIP 1997. Lecture Notes in Computer Science, vol 1296. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63460-6_131
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DOI: https://doi.org/10.1007/3-540-63460-6_131
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