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

Volume

5

Issue

1

fYear

1996

fDate

1/1/1996 12:00:00 AM

Firstpage

171

Lastpage

175

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;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/83.481685

Filename

481685