STEREO MATCHING BASED ON THE SELF-ORGANIZING FEATURE-MAPPING ALGORITHM

This paper presents an approach to the local stereo matching problem u sing edge segments as features with several attributes. We have verifi ed that the differences in attributes for the true matches cluster in a cloud around a center. The correspondence is established on the basi s of the minimum squared Mahalanobis distance between the difference o f the attributes for a current pair of Features and the cluster center (similarity constraint). We introduce a learning strategy based on th e Self-Organizing feature-mapping method to get the best cluster cente r. A comparative analysis among methods without learning is illustrate d. (C) 1998 Elsevier Science B.V. All rights reserved.