Binocular Stereo by Maximizing the Likelihood Ratio Relative to a Random Terrain
A novel approach to computational binocular stereo based on the Neyman-Pearson criterion for discriminating between statistical hypotheses is proposed. An epipolar terrain profile is reconstructed by maximizing its likelihood ratio with respect to a purely random profile. A simple generative Markov-chain model of an image-driven profile that extends the model of a random profile is introduced. The extended model relates transition probabilities for binocularly and monocularly visible points along the profile to grey level differences between corresponding pixels in mutually adapted stereo images. This allows for regularizing the ill-posed stereo problem with respect to partial occlusions.
KeywordsStereo Image Partial Occlusion Stereo Match Stereo Pair Stationary Markov Chain
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