MAP-Based Stochastic Diffusion for Stereo Matching and Line Fields Estimation
International Journal of Computer Vision
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This paper argues that the disparity gradient subsumes various constraints for stereo matching, and can thus be used as the basis of a unified cooperative stereo algorithm. Traditionally, selection of the neighborhood support function (NSF) in cooperative stereo was left as a heuristic exercise. We present an analysis and evaluation of three families of NSFs based on the disparity gradient. It is shown that an exponential decay function with a conveniently selectable parameter is well behaved in that it yields the least error, converges steadily, and produces correctly located weak-winners. The discovery of the well-behaved function facilitates the success of the disparity gradient based approach. It is suggested that this function will help a two-pass algorithm in resolving the dilemma of surface continuity and discontinuity/occlusion. In our experiments, the unified cooperative stereo-matching algorithm is tested on random-dot stereograms containing opaque and transparent surfaces. It is also shown to be applicable to both area matching and contour matching in real-world images