Abstract
The connectionist approach to the recovery of 3-D shape information from 2-D images developed by the authors, is based on a system made up by two cascaded neural networks. The first network is an implementation of the BCS, an architecture which derives from a biological model of the low level visual processes developed by Grossberg and Mingolla: this architecture extracts a sort of brightness gradient map from the image. The second network is a backpropagation architecture that supplies an estimate of the geometric parameters of the objects in the scene under consideration, starting from the outputs of the BCS. A detailed description of the system and the experimental results obtained by simulating it are reported in the paper.
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© 1991 Springer-Verlag Berlin Heidelberg
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Ardizzone, E., Chella, A., Pirrone, R., Sorbello, F. (1991). A system based on neural architectures for the reconstruction of 3-D shapes from images. In: Ardizzone, E., Gaglio, S., Sorbello, F. (eds) Trends in Artificial Intelligence. AI*IA 1991. Lecture Notes in Computer Science, vol 549. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-54712-6_242
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DOI: https://doi.org/10.1007/3-540-54712-6_242
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