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.