Enhanced Clustering Algorithms For Gray-Scale Image Segmentation

The clustering algorithms are widely used as an unsupervised method for image segmentation in medical diagnosis, satellite imaging and biometric systems. The algorithms are chosen since they are easy to be implemented, required low computational time and less sensitive to noise and artifacts. Howeve...

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Main Author:	Siddiqui, Fasahat Ullah
Format:	Thesis
Language:	English
Published:	2012
Subjects:	TK1-9971 Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.usm.my/41804/1/FASAHAT_ULLAH_SIDDIQUI.pdf
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id	my-usm-ep.41804
record_format	uketd_dc
spelling	my-usm-ep.418042019-04-12T05:26:22Z Enhanced Clustering Algorithms For Gray-Scale Image Segmentation 2012-04 Siddiqui, Fasahat Ullah TK1-9971 Electrical engineering. Electronics. Nuclear engineering The clustering algorithms are widely used as an unsupervised method for image segmentation in medical diagnosis, satellite imaging and biometric systems. The algorithms are chosen since they are easy to be implemented, required low computational time and less sensitive to noise and artifacts. However, in some cases the conventional clustering algorithms introduce over-segmentation problems and unable to preserve the region of interest (i.e. objects). 2012-04 Thesis http://eprints.usm.my/41804/ http://eprints.usm.my/41804/1/FASAHAT_ULLAH_SIDDIQUI.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Eletrik & Elektronik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	TK1-9971 Electrical engineering Electronics Nuclear engineering
spellingShingle	TK1-9971 Electrical engineering Electronics Nuclear engineering Siddiqui, Fasahat Ullah Enhanced Clustering Algorithms For Gray-Scale Image Segmentation
description	The clustering algorithms are widely used as an unsupervised method for image segmentation in medical diagnosis, satellite imaging and biometric systems. The algorithms are chosen since they are easy to be implemented, required low computational time and less sensitive to noise and artifacts. However, in some cases the conventional clustering algorithms introduce over-segmentation problems and unable to preserve the region of interest (i.e. objects).
format	Thesis
qualification_level	Master's degree
author	Siddiqui, Fasahat Ullah
author_facet	Siddiqui, Fasahat Ullah
author_sort	Siddiqui, Fasahat Ullah
title	Enhanced Clustering Algorithms For Gray-Scale Image Segmentation
title_short	Enhanced Clustering Algorithms For Gray-Scale Image Segmentation
title_full	Enhanced Clustering Algorithms For Gray-Scale Image Segmentation
title_fullStr	Enhanced Clustering Algorithms For Gray-Scale Image Segmentation
title_full_unstemmed	Enhanced Clustering Algorithms For Gray-Scale Image Segmentation
title_sort	enhanced clustering algorithms for gray-scale image segmentation
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Eletrik & Elektronik
publishDate	2012
url	http://eprints.usm.my/41804/1/FASAHAT_ULLAH_SIDDIQUI.pdf
_version_	1747820973471563776

Enhanced Clustering Algorithms For Gray-Scale Image Segmentation

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