Multi-threshold image segmentation based on two-dimensional Tsallis

Multi-threshold image segmentation based on two-dimensional Tsallis

Image multi-threshold segmentation method based on two-dimensional Tsallis entropy is proposed by utilizing Tsallis entropy. The improved particle swarm optimization is used to search best two-dimensional multi-threshold vectors by maximising the two-dimensional Tsallis entropy. The proposed method...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Xu Dong, Tang Xu-dong
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Image segmentation
IPSO
multithreshold
Tsallis entropy
Vehicles
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 5
container_issue
container_start_page 1
container_title
container_volume 6
creator Xu Dong
Tang Xu-dong
description Image multi-threshold segmentation method based on two-dimensional Tsallis entropy is proposed by utilizing Tsallis entropy. The improved particle swarm optimization is used to search best two-dimensional multi-threshold vectors by maximising the two-dimensional Tsallis entropy. The proposed method not only considers the spatial information of pixels, but also the interaction between the object and background, the different responses in variant grey level. The experimental results show that the new algorithm is better than the tradition methods with both a better stability and a higher speed.
doi_str_mv 10.1109/ICCSIT.2010.5563584
format Conference Proceeding
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_5563584</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5563584</ieee_id><sourcerecordid>5563584</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-cf845bb09bea0827056850580a5e6a8c48cdcf67afa7d3ea060415218c1fb1c03</originalsourceid><addsrcrecordid>eNpNUMlOwzAQNUKVgDZf0Et-IGW8TGwfUcQSqYgD6blyHKc1chsUGyH-Hkv0wLvMW0ZPoyFkTWFDKej7tmne227DIBuINUclrkihpaKCCYEoQF__11zqBbljAFpzxQW7IUWMH5AhkKFit6R5_QrJV-k4u3icwlD6kzm4MrrDyZ2TSX46l72JbigzSd9TNfgcxGybUHbRhODjiixGE6IrLnNJdk-PXfNSbd-e2-ZhW3kqMVV2VAL7HnTvDCgmAWuFgAoMutooK5Qd7FhLMxo58LxTg6DIqLJ07KkFviTrv17vnNt_zvnU-Wd_-QP_Be37T3M</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Multi-threshold image segmentation based on two-dimensional Tsallis</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Xu Dong ; Tang Xu-dong</creator><creatorcontrib>Xu Dong ; Tang Xu-dong</creatorcontrib><description>Image multi-threshold segmentation method based on two-dimensional Tsallis entropy is proposed by utilizing Tsallis entropy. The improved particle swarm optimization is used to search best two-dimensional multi-threshold vectors by maximising the two-dimensional Tsallis entropy. The proposed method not only considers the spatial information of pixels, but also the interaction between the object and background, the different responses in variant grey level. The experimental results show that the new algorithm is better than the tradition methods with both a better stability and a higher speed.</description><identifier>ISBN: 9781424455379</identifier><identifier>ISBN: 1424455375</identifier><identifier>EISBN: 9781424455409</identifier><identifier>EISBN: 9781424455393</identifier><identifier>EISBN: 1424455405</identifier><identifier>EISBN: 1424455391</identifier><identifier>DOI: 10.1109/ICCSIT.2010.5563584</identifier><identifier>LCCN: 2009938342</identifier><language>eng</language><publisher>IEEE</publisher><subject>Image segmentation ; IPSO ; multithreshold ; Tsallis entropy ; Vehicles</subject><ispartof>2010 3rd International Conference on Computer Science and Information Technology, 2010, Vol.6, p.1-5</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5563584$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5563584$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Xu Dong</creatorcontrib><creatorcontrib>Tang Xu-dong</creatorcontrib><title>Multi-threshold image segmentation based on two-dimensional Tsallis</title><title>2010 3rd International Conference on Computer Science and Information Technology</title><addtitle>ICCSIT</addtitle><description>Image multi-threshold segmentation method based on two-dimensional Tsallis entropy is proposed by utilizing Tsallis entropy. The improved particle swarm optimization is used to search best two-dimensional multi-threshold vectors by maximising the two-dimensional Tsallis entropy. The proposed method not only considers the spatial information of pixels, but also the interaction between the object and background, the different responses in variant grey level. The experimental results show that the new algorithm is better than the tradition methods with both a better stability and a higher speed.</description><subject>Image segmentation</subject><subject>IPSO</subject><subject>multithreshold</subject><subject>Tsallis entropy</subject><subject>Vehicles</subject><isbn>9781424455379</isbn><isbn>1424455375</isbn><isbn>9781424455409</isbn><isbn>9781424455393</isbn><isbn>1424455405</isbn><isbn>1424455391</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpNUMlOwzAQNUKVgDZf0Et-IGW8TGwfUcQSqYgD6blyHKc1chsUGyH-Hkv0wLvMW0ZPoyFkTWFDKej7tmne227DIBuINUclrkihpaKCCYEoQF__11zqBbljAFpzxQW7IUWMH5AhkKFit6R5_QrJV-k4u3icwlD6kzm4MrrDyZ2TSX46l72JbigzSd9TNfgcxGybUHbRhODjiixGE6IrLnNJdk-PXfNSbd-e2-ZhW3kqMVV2VAL7HnTvDCgmAWuFgAoMutooK5Qd7FhLMxo58LxTg6DIqLJ07KkFviTrv17vnNt_zvnU-Wd_-QP_Be37T3M</recordid><startdate>201007</startdate><enddate>201007</enddate><creator>Xu Dong</creator><creator>Tang Xu-dong</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201007</creationdate><title>Multi-threshold image segmentation based on two-dimensional Tsallis</title><author>Xu Dong ; Tang Xu-dong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-cf845bb09bea0827056850580a5e6a8c48cdcf67afa7d3ea060415218c1fb1c03</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Image segmentation</topic><topic>IPSO</topic><topic>multithreshold</topic><topic>Tsallis entropy</topic><topic>Vehicles</topic><toplevel>online_resources</toplevel><creatorcontrib>Xu Dong</creatorcontrib><creatorcontrib>Tang Xu-dong</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Xu Dong</au><au>Tang Xu-dong</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Multi-threshold image segmentation based on two-dimensional Tsallis</atitle><btitle>2010 3rd International Conference on Computer Science and Information Technology</btitle><stitle>ICCSIT</stitle><date>2010-07</date><risdate>2010</risdate><volume>6</volume><spage>1</spage><epage>5</epage><pages>1-5</pages><isbn>9781424455379</isbn><isbn>1424455375</isbn><eisbn>9781424455409</eisbn><eisbn>9781424455393</eisbn><eisbn>1424455405</eisbn><eisbn>1424455391</eisbn><abstract>Image multi-threshold segmentation method based on two-dimensional Tsallis entropy is proposed by utilizing Tsallis entropy. The improved particle swarm optimization is used to search best two-dimensional multi-threshold vectors by maximising the two-dimensional Tsallis entropy. The proposed method not only considers the spatial information of pixels, but also the interaction between the object and background, the different responses in variant grey level. The experimental results show that the new algorithm is better than the tradition methods with both a better stability and a higher speed.</abstract><pub>IEEE</pub><doi>10.1109/ICCSIT.2010.5563584</doi><tpages>5</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISBN: 9781424455379
ispartof 2010 3rd International Conference on Computer Science and Information Technology, 2010, Vol.6, p.1-5
issn
language eng
recordid cdi_ieee_primary_5563584
source IEEE Electronic Library (IEL) Conference Proceedings
subjects Image segmentation
IPSO
multithreshold
Tsallis entropy
Vehicles
title Multi-threshold image segmentation based on two-dimensional Tsallis
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-29T10%3A18%3A27IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Multi-threshold%20image%20segmentation%20based%20on%20two-dimensional%20Tsallis&rft.btitle=2010%203rd%20International%20Conference%20on%20Computer%20Science%20and%20Information%20Technology&rft.au=Xu%20Dong&rft.date=2010-07&rft.volume=6&rft.spage=1&rft.epage=5&rft.pages=1-5&rft.isbn=9781424455379&rft.isbn_list=1424455375&rft_id=info:doi/10.1109/ICCSIT.2010.5563584&rft_dat=%3Cieee_6IE%3E5563584%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=9781424455409&rft.eisbn_list=9781424455393&rft.eisbn_list=1424455405&rft.eisbn_list=1424455391&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5563584&rfr_iscdi=true