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- W2105637862 abstract "A stochastic optimization approach to stereo matching is presented. Unlike conventional correlation matching and feature matching, the approach provides a dense array of disparities, eliminating the need for interpolation. First, the stereo matching problem is defined in terms of finding a disparity map that satisfies two competing constraints: (1) matched points should have similar image intensity, and (2) the disparity map should be smooth. These constraints are expressed in an function that can be evaluated locally. A simulated annealing algorithm is used to find a disparity map that has very low energy (i.e., in which both constraints have simultaneously been approximately satisfied). Annealing allows the large-scale structure of the disparity map to emerge at higher temperatures, and avoids the problem of converging too quickly on a local minimum. Results are shown for a sparse random-dot stereogram, a vertical aerial stereogram (shown in comparison to ground truth), and an oblique ground-level scene with occlusion boundaries." @default.
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- W2105637862 date "1987-01-01" @default.
- W2105637862 modified "2023-09-28" @default.
- W2105637862 title "A STOCHASTIC APPROACH TO STEREO VISION" @default.
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- W2105637862 doi "https://doi.org/10.1016/b978-0-08-051581-6.50008-8" @default.
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