Recently, it was shown how some metaphors, adopted from the infant vision system, were useful for face recognition. In this paper we adopt those biological hypotheses and apply them to the 3D object recognition problem. As the infant vision responds to low frequencies of the signal, a low-filter is used to remove high frequency components from the image. Then we detect subtle features in the image by means of a random feature selection detector. At last, a dynamic associative memory (DAM) is fed with this information for training and recognition. To test the accuracy of the proposal we use the Columbia Object Image Library (COIL 100). © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Vázquez, R. A., Sossa, H., & Garro, B. A. (2009). The role of the infant vision system in 3d object recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5507 LNCS, pp. 800–807). https://doi.org/10.1007/978-3-642-03040-6_98
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