Abstract
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.
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Pajares, G., de la Cruz, J.M., López, J.A. (1998). Pattern recognition learning applied to stereovision matching. In: Amin, A., Dori, D., Pudil, P., Freeman, H. (eds) Advances in Pattern Recognition. SSPR /SPR 1998. Lecture Notes in Computer Science, vol 1451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033330
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DOI: https://doi.org/10.1007/BFb0033330
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