Abstract
In this paper a system based on the use of local schemes of representation is presented in order to model the receptive visual field responses observed in the retinal ganglion cells, lateral geniculate nucleus (LGN) and the primary visual cortex. The use of cellular neural networks (CNN) has been considered in particular as an efficient means to compute the Gabor filtering processes. The CNNs are structures of computation composed of arrays of cells interconnected taking advantage of the parallelism by assigning a processor (cell) to every image pixel. Each cell, having an internal state receives inputs from a predefined neighborhood, evolves in continuous time and assumes a continuum of values. The present scheme implements a logarithmic multiresolution structure of computation (pyramid), and constitutes a biological plausible model for implementing the spatial frequency and orientation selectivity mechanisms observed in early vision processing
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Cristobal, G. (1992). Receptive field image modeling through cellular neural networks. In: Eeckman, F.H. (eds) Analysis and Modeling of Neural Systems. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4010-6_24
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DOI: https://doi.org/10.1007/978-1-4615-4010-6_24
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