A fast learning algorithm for Gabor transformation
Date
1996
Authors
Ibrahim, Ayman, author
Azimi-Sadjadi, Mahmood R., author
IEEE, publisher
Journal Title
Journal ISSN
Volume Title
Abstract
An adaptive learning approach for the computation of the coefficients of the generalized nonorthogonal 2-D Gabor transform representation is introduced in this correspondence. 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.
Description
Rights Access
Subject
image representation
convergence of numerical methods
learning systems
adaptive systems
transforms
data compression
least squares approximations
recursive estimation
data reduction
image reconstruction