Daugman's Gabor transform as a simple generative back-propagation network

Abstract

Much work has been performed on learning mechanisms for neural networks. A particular area of interest has been the use of neural networks for image processing problems. Two important pieces of work in this area are unified. An architecture and learning scheme for neural networks called generative back propagation has been previously developed and a system for image compression and filtering based on 2-D Gabor transformations which used a neural network type architecture described. Daugman's procedure is exactly replicated, a procedure which used a four layer neural network as a two-layer generative back propagation network with half of the units. The GBP update rule is shown to perform the same change as Daugman's rule, but more efficiently.

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