Weight adjustment rule of neural networks for computing discrete 2-D Gabor transforms (image processing)

It is demonstrated that the weight adjustment rule used in the neural network for computing the 2-D Gabor transform proposed by J. Daugman (1988) can be shown to be equivalent to the Jacobi iteration scheme for solving simultaneous linear equations. It is shown that faster convergence, of the algorithm can be achieved by using Gauss-Seidel iteration, successive overrelaxation, conjugate gradient algorithms, and multigrid methods.<
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