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Scharstein Daniel,Szeliski Richard




Stereo Matching with Non-Linear Diffusion



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abstract:

One of the central problems in stereo matching (and other image registration tasks) is the selection of optimal window sizes for comparing image regions. This paper addresses this problem with some novel algorithms based on iteratively di using support a t different disparity hypotheses, and locally controlling the amount of di usion based on the current quality of the disparity estimate. It also develops a novel Bayesian estimation technique which signi cantly outperforms techniques based on area-based matching (SSD) and regular di usion. We provide experimental results on both synthetic and real stereo image pairs

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