A new learning strategy for stereo matching derived from a fuzzy clustering method

This paper presents an approach to the local stereo correspondence problem. The primitives or features used are groups of collinear connected edge poi nts called segments. Each segment has several associated attributes or prop erties. We have verified that the differences of the attributes for the tru e matches cluster in a cloud around a center. Then for each current pair of primitives we compute a distance between the difference of its attributes and the cluster center. The correspondence is established in the basis of t he minimum distance criterion (similarity constraint). We have designed an image understanding system to learn the best representative cluster center. For such purpose a new learning method is derived from the Fuzzy c-Means ( FcM) algorithm where the dispersion of the true samples in the cluster is t aken into account through the Mahalanobis distance. This is the main contri bution of this paper. A better performance of the proposed local stereo-mat ching learning method is illustrated with a comparative analysis between cl assical local methods without learning. (C) 2000 Elsevier Science B.V. All rights reserved.