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• W2002682020 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 distance criterion, computing the 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 Hebbian Learning to get the best cluster center. A comparative analysis among methods without learning and with other learning strategies is illustrated." @default.
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• W2002682020 date "1999-01-01" @default.
• W2002682020 modified "2023-10-02" @default.
• W2002682020 title "Stereo matching using Hebbian learning" @default.
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• W2002682020 doi "https://doi.org/10.1109/3477.775274" @default.
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