Frequency adaptive learning SOFM for grayscale image compression

A new learning algorithm is proposed where the learning constant is based on the localized winning frequency of each individual neuron. Blocks of sub-image are fed in a butterfly-jumping sequence during the training process. Untrained neurons are also re-initialized.

Saved in:

Bibliographic Details
Main Author:	Tan, Chiang Poo.
Other Authors:	Chang, Chip Hong
Format:	Theses and Dissertations
Published:	2008
Subjects:	DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Online Access:	http://hdl.handle.net/10356/3336
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Nanyang Technological University

id	sg-ntu-dr.10356-3336
record_format	dspace
spelling	sg-ntu-dr.10356-33362023-07-04T15:53:27Z Frequency adaptive learning SOFM for grayscale image compression Tan, Chiang Poo. Chang, Chip Hong School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing A new learning algorithm is proposed where the learning constant is based on the localized winning frequency of each individual neuron. Blocks of sub-image are fed in a butterfly-jumping sequence during the training process. Untrained neurons are also re-initialized. Master of Science (Consumer Electronics) 2008-09-17T09:27:42Z 2008-09-17T09:27:42Z 2003 2003 Thesis http://hdl.handle.net/10356/3336 Nanyang Technological University application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
topic	DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Tan, Chiang Poo. Frequency adaptive learning SOFM for grayscale image compression
description	A new learning algorithm is proposed where the learning constant is based on the localized winning frequency of each individual neuron. Blocks of sub-image are fed in a butterfly-jumping sequence during the training process. Untrained neurons are also re-initialized.
author2	Chang, Chip Hong
author_facet	Chang, Chip Hong Tan, Chiang Poo.
format	Theses and Dissertations
author	Tan, Chiang Poo.
author_sort	Tan, Chiang Poo.
title	Frequency adaptive learning SOFM for grayscale image compression
title_short	Frequency adaptive learning SOFM for grayscale image compression
title_full	Frequency adaptive learning SOFM for grayscale image compression
title_fullStr	Frequency adaptive learning SOFM for grayscale image compression
title_full_unstemmed	Frequency adaptive learning SOFM for grayscale image compression
title_sort	frequency adaptive learning sofm for grayscale image compression
publishDate	2008
url	http://hdl.handle.net/10356/3336
_version_	1772825515905777664

Frequency adaptive learning SOFM for grayscale image compression

Similar Items