A stereo matching algorithm based on color segments

Traditionally, the problem of stereo matching has been addressed either by a sparse feature-based approach or a dense area-based approach. This technique still suffers from a lack in accuracy or long computation time needed to match stereo images. In this paper, we employ color segments on scan lines of stereo images as primitives for matching, and introduce the widely used fuzzy set theory to the matching processes in order to handle ambiguities in matching. During the stage of accurate pixel correspondences of matched color segments, we use the technique of dynamic time warping (DTW), which is used in speech recognition. We found that DTW works robustly and rapidly regardless of the number of points to be matched. Covering more image space than point elements and providing more information than gray level features, color segments are less sensitive to noise and photometric variations and are thus more easily matched, and can reduce the false matches. Furthermore, a post-processing step can find more matches and alleviate the influence of under-segmentation or over-segmentation. A fast median filter can be applied to the results to further remove outliers. Experiments have been performed to demonstrate the accuracy and efficiency of the algorithm.