Abstract
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).
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Vázquez, R.A., Sossa, H., Garro, B.A. (2009). The Role of the Infant Vision System in 3D Object Recognition. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_98
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DOI: https://doi.org/10.1007/978-3-642-03040-6_98
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