Title :
A fast learning algorithm for Gabor transformation
Author :
Ibrahim, Ayman ; Azimi-Sadjadi, Mahmood R.
Author_Institution :
Dept. of Electr. Eng., Colorado State Univ., Fort Collins, CO, USA
fDate :
1/1/1996 12:00:00 AM
Abstract :
An adaptive learning approach for the computation of the coefficients of the generalized nonorthogonal 2-D Gabor (1946) transform representation is introduced. The algorithm uses a recursive least squares (RLS) type algorithm. The aim is to achieve minimum mean squared error for the reconstructed image from the set of the Gabor coefficients. The proposed RLS learning offers better accuracy and faster convergence behavior when compared with the least mean squares (LMS)-based algorithms. Applications of this scheme in image data reduction are also demonstrated
Keywords :
adaptive systems; convergence of numerical methods; data compression; data reduction; image reconstruction; image representation; learning systems; least squares approximations; recursive estimation; transforms; Gabor coefficients; Gabor transformation; RLS algorithm; RLS learning; convergence; fast learning algorithm; image data reduction; image reconstruction; minimum mean squared error; nonorthogonal 2D Gabor transform representation; recursive least squares; Convergence; Fourier transforms; Image reconstruction; Least squares approximation; Least squares methods; Resonance light scattering; Shape; Signal analysis; Sparse matrices; Uncertainty;
Journal_Title :
Image Processing, IEEE Transactions on
