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A fast learning algorithm for Gabor transform with applications to image data reduction and pattern classification

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3 Author(s)
Ibrahim, A.E. ; Dept. of Electr. Eng., Colorado State Univ., Fort Collins, CO, USA ; Sadjadi, M.R.A. ; Sheedvash, S.

A simple neural network-based approach is introduced, which allows the computation of the coefficients of the generalized non-orthogonal 2D Gabor transform representation. The network is trained using a recursive least squares (RLS) type algorithm. This RLS learning algorithm offers better accuracy and faster convergence when compared to the least mean squares based algorithms. The aim is to achieve minimum mean squared error for the reconstructed image from the set of the Gabor coefficients. Applications of this scheme in image data reduction and pattern classification are demonstrated in the simulation results

Published in:

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on  (Volume:7 )

Date of Conference:

27 Jun-2 Jul 1994

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