A Parallel Matching Algorithm for Stereo Vision

This paper proposes a parallel matching algorithm for feature-based stereo vision. Features are zero-crossing (ZC) points detected with various sizes of Laplacian-Gaussian filters. In order to obtain candidate intervals of disparity, the disparity histogram is computed all over the image. The image is, then, divided into small areas and the disparity histogram in each local area is computed within the candidate intervals. The local disparity histograms in all the channels are fed to the fusion evaluates and the most probable disparity is detected in each local area. Once the most probable disparity is detected, disparities for all the finest ZC points arc determined in the local area to obtain a high resolution disparity map. The, matching pairs are removed from a set of ZC points. A series of processes are iterated until no more disparities are determined. Experiments with a sample scene reveals that the algorithm has advantages in efficiency and performance.