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Proceedings Paper

Robust stereo vision
Author(s): Saravajit Sahay Sinha; Saied Moezzi; Brian G. Schunck
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Paper Abstract

This paper presents an algorithm for visual surface reconstruction which makes use of a robust local reconstruction scheme to produce a dense disparity map from a multiresolution feature-based stereo matching algorithm. Robustness implies that the algorithm can reject large amounts of outliers in the disparities which are caused by mismatches in the correspondence process while simultaneously preserving discontinuities in the depth. Our robust algorithm uses a standard multiresolution stereo algorithm in conjunction with a moving least median of squares (MLMS) algorithm to fit local planar patches to the disparity functional at each level of the multiresolution pyramid. The MLMS algorithm finds the best fit by minimizing the median of the error between the fit and the data. By applying the MLMS algorithm at each stage of the pyramid we not only create a denser grid at each level but also " nip in the bud" any errors which occur at a coarse level before they are propagated to finer levels of the multiresolution process. Experimental results are presented on real and synthetic data.

Paper Details

Date Published: 1 March 1991
PDF: 9 pages
Proc. SPIE 1385, Optics, Illumination, and Image Sensing for Machine Vision V, (1 March 1991); doi: 10.1117/12.25365
Show Author Affiliations
Saravajit Sahay Sinha, Univ. of Michigan (United States)
Saied Moezzi, Univ. of Michigan (United States)
Brian G. Schunck, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 1385:
Optics, Illumination, and Image Sensing for Machine Vision V
Donald J. Svetkoff; Kevin G. Harding; Gordon T. Uber; Norman Wittels, Editor(s)

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