An efficient and robust cluster-based algorithm for image duplicated region detection using K-means clusteringTafreshi

An efficient and robust cluster-based algorithm for image duplicated region detection using K-means clusteringTafreshi

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Main Author: Tafreshi, Amir Esmaeil Sarabadani
Format: Thesis
Published: 2013
Subjects:
QA Mathematics
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id my-utm-ep.41587
record_format uketd_dc
spelling my-utm-ep.415872014-10-08T02:21:01Z An efficient and robust cluster-based algorithm for image duplicated region detection using K-means clusteringTafreshi 2013 Tafreshi, Amir Esmaeil Sarabadani QA Mathematics 2013 Thesis http://eprints.utm.my/id/eprint/41587/ masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution Universiti Teknologi Malaysia
collection UTM Institutional Repository
topic QA Mathematics
spellingShingle QA Mathematics
Tafreshi, Amir Esmaeil Sarabadani
An efficient and robust cluster-based algorithm for image duplicated region detection using K-means clusteringTafreshi
description
format Thesis
qualification_level Master's degree
author Tafreshi, Amir Esmaeil Sarabadani
author_facet Tafreshi, Amir Esmaeil Sarabadani
author_sort Tafreshi, Amir Esmaeil Sarabadani
title An efficient and robust cluster-based algorithm for image duplicated region detection using K-means clusteringTafreshi
title_short An efficient and robust cluster-based algorithm for image duplicated region detection using K-means clusteringTafreshi
title_full An efficient and robust cluster-based algorithm for image duplicated region detection using K-means clusteringTafreshi
title_fullStr An efficient and robust cluster-based algorithm for image duplicated region detection using K-means clusteringTafreshi
title_full_unstemmed An efficient and robust cluster-based algorithm for image duplicated region detection using K-means clusteringTafreshi
title_sort efficient and robust cluster-based algorithm for image duplicated region detection using k-means clusteringtafreshi
granting_institution Universiti Teknologi Malaysia, Faculty of Computing
granting_department Faculty of Computing
publishDate 2013
_version_ 1747816576778764288

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