The University of Southampton
University of Southampton Institutional Repository

Daugman's Gabor transform as a simple generative back-propagation network

Daugman's Gabor transform as a simple generative back-propagation network
Daugman's Gabor transform as a simple generative back-propagation network
Much work has been performed on learning mechanisms for neural networks. A particular area of interest has been the use of neural networks for image processing problems. Two important pieces of work in this area are unified. An architecture and learning scheme for neural networks called generative back propagation has been previously developed and a system for image compression and filtering based on 2-D Gabor transformations which used a neural network type architecture described. Daugman's procedure is exactly replicated, a procedure which used a four layer neural network as a two-layer generative back propagation network with half of the units. The GBP update rule is shown to perform the same change as Daugman's rule, but more efficiently.
0013-5194
1241-1243
Cohen, D.
a7633443-d42d-4db7-93a2-f7173d01a71d
Shawe-Taylor, J.
c32d0ee4-b422-491f-8c28-78663851d6db
Cohen, D.
a7633443-d42d-4db7-93a2-f7173d01a71d
Shawe-Taylor, J.
c32d0ee4-b422-491f-8c28-78663851d6db

Cohen, D. and Shawe-Taylor, J. (1990) Daugman's Gabor transform as a simple generative back-propagation network. Electronics Letters, 26 (16), 1241-1243.

Record type: Article

Abstract

Much work has been performed on learning mechanisms for neural networks. A particular area of interest has been the use of neural networks for image processing problems. Two important pieces of work in this area are unified. An architecture and learning scheme for neural networks called generative back propagation has been previously developed and a system for image compression and filtering based on 2-D Gabor transformations which used a neural network type architecture described. Daugman's procedure is exactly replicated, a procedure which used a four layer neural network as a two-layer generative back propagation network with half of the units. The GBP update rule is shown to perform the same change as Daugman's rule, but more efficiently.

PDF
DaugmansGaborTransformSimpleGenerative.pdf - Other
Download (236kB)

More information

Published date: August 1990
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 259829
URI: https://eprints.soton.ac.uk/id/eprint/259829
ISSN: 0013-5194
PURE UUID: ae49d6f4-e3c9-4abb-b949-594f46cdd24b

Catalogue record

Date deposited: 24 Aug 2004
Last modified: 18 Jul 2017 09:19

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×