Pattern Recognition Learning Applied to Stereovision Matching

This paper presents an approach to the local stereovision matching problem by developing a statistical pattern recognition learning strategy. We use 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 a maximum likelihood estimates method to get the best cluster center. A comparative analysis against a classical approach using the squared Euclidean distance (i.e. without learning) is illustrated.