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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

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Subject

image representation
convergence of numerical methods
learning systems
adaptive systems
transforms
data compression
least squares approximations
recursive estimation
data reduction
image reconstruction

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