Stereo matching using Hebbian learning

This paper presents an approach to the local stereo matching problem using edge segments as features with several attributes. We have verified that th e differences in attributes for the true matches cluster in a cloud around a center. The correspondence is established on the basis of the minimum dis tance criterion, computing the Mahalanobis distance between the difference of the attributes for a current pair of features and the cluster center (si milarity constraint). We introduce a learning strategy based on the Hebbian learning to get the best cluster center. A comparative analysis among meth ods without learning and with other learning strategies is illustrated.