Pajares, G.; Cruz, J. M.; Aranda, J. Stereo matching based on the self-organizing feature-mapping algorithm. (English) Zbl 0905.68118 Pattern Recognit. Lett. 19, No. 3-4, 319-330 (1998). Summary: 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 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 the self-organizing feature-mapping method to get the best cluster center. A comparative analysis among methods without learning is illustrated. Cited in 1 Document MSC: 68T05 Learning and adaptive systems in artificial intelligence Keywords:stereo matching problem PDFBibTeX XMLCite \textit{G. Pajares} et al., Pattern Recognit. Lett. 19, No. 3--4, 319--330 (1998; Zbl 0905.68118) Full Text: DOI