Stereo matching based on the self-organizing feature-mapping algorithm

Abstract

This paper presents an approach to the local stereo matching problem using edge segments as features with several attributes. We have verified that the differences in attributes for the true matches cluster in a cloud around a center. The correspondence is established on the basis of the minimum squared Mahalanobis distance between the difference of the attributes for a current pair of features and the cluster center (similarity constraint). We introduce a learning strategy based on the Self-Organizing feature-mapping method to get the best cluster center. A comparative analysis among methods without learning is illustrated.