Segmentation d'une image couleur par les critères d'information et la théorie des ensembles flous
Identifieur interne : 000696 ( Hal/Checkpoint ); précédent : 000695; suivant : 000697Segmentation d'une image couleur par les critères d'information et la théorie des ensembles flous
Auteurs : H. Hamzaouil [Maroc] ; A. Elmatouat [France] ; P. Martin [France]Source :
- Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées [ 1638-5713 ] ; 2006.
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Abstract
In this paper we present an unsupervised color image segmentation algorithm using the information criteria and a fuzzy theory. We propose this method to estimate the number of color image clusters and the optimal radius associated with minimizing the value of the proposed criteria. The experimental results demonstrate that this approach compresses the image in a small number of clusters without losing the informational contents of the image and we reduce the number of parameters using the process of segmentation, we also decrease the computational time. The color image segmentation system has been tested on some usual color images; "House", "Lena", "Monarch" and "Peppers".
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Hamzaouil</name><affiliation wicri:level="1"><hal:affiliation type="institution" xml:id="struct-447007" status="VALID"><orgName>Faculté des sciences</orgName><orgName type="acronym">Fès</orgName><desc><address><addrLine>Faculté des sciences, Fès, Maroc </addrLine><country key="MA"></country></address><ref type="url">http://www.fsdmfes.ac.ma/contact.php</ref></desc></hal:affiliation><country>Maroc</country></affiliation></author><author><name sortKey="Elmatouat, A" sort="Elmatouat, A" uniqKey="Elmatouat A" first="A." last="Elmatouat">A. 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Martin</name><affiliation wicri:level="1"><hal:affiliation type="laboratory" xml:id="struct-447173" status="VALID"><orgName>Laboratoire de Microélectronique </orgName><orgName type="acronym">LaMIP</orgName><desc><address><addrLine>Ensicaen NXP (LaMIP),CAEN</addrLine><country key="FR"></country></address></desc><listRelation><relation active="#struct-331458" type="direct"></relation></listRelation><tutelles><tutelle active="#struct-331458" type="direct"><org type="institution" xml:id="struct-331458" status="VALID"><orgName>Laboratoire de microélectronique et physique des semiconducteurs</orgName><orgName type="acronym">LAMIPS</orgName><date type="start">2000</date><desc><address><addrLine>2 esplanade Campus Effiscience Colombelles 14906 Caen</addrLine><country key="FR"></country></address></desc></org></tutelle></tutelles></hal:affiliation><country>France</country></affiliation></author></analytic><series><title level="j">Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées</title><idno type="ISSN">1638-5713</idno><imprint><date type="datePub">2006</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term> Fuzzy Classification Algorithm</term><term> Fuzzy sets</term><term> Histogram</term><term>Information Criteria</term></keywords><keywords scheme="mix" xml:lang="fr"><term>Algorithme de classification floue</term><term>Critères d'information</term><term>Ensembles flous</term><term>Histogramme</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In this paper we present an unsupervised color image segmentation algorithm using the information criteria and a fuzzy theory. We propose this method to estimate the number of color image clusters and the optimal radius associated with minimizing the value of the proposed criteria. The experimental results demonstrate that this approach compresses the image in a small number of clusters without losing the informational contents of the image and we reduce the number of parameters using the process of segmentation, we also decrease the computational time. The color image segmentation system has been tested on some usual color images; "House", "Lena", "Monarch" and "Peppers".</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="fr">Segmentation d'une image couleur par les critères d'information et la théorie des ensembles flous</title><author role="aut"><persName><forename type="first">H.</forename><surname>Hamzaouil</surname></persName><email></email><idno type="halauthor">1282643</idno><affiliation ref="#struct-447007"></affiliation></author><author role="aut"><persName><forename type="first">A.</forename><surname>Elmatouat</surname></persName><email></email><idno type="halauthor">1282644</idno><affiliation ref="#struct-447168"></affiliation></author><author role="aut"><persName><forename type="first">P.</forename><surname>Martin</surname></persName><email></email><idno type="halauthor">847</idno><affiliation ref="#struct-447173"></affiliation></author><editor role="depositor"><persName><forename>Coordination</forename><surname>Episciences Iam</surname></persName><email>episciences@inria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2016-01-27 17:15:38</date><date type="whenModified">2016-04-18 17:06:08</date><date type="whenReleased">2016-04-18 17:06:08</date><date type="whenProduced">2006</date><date type="whenEndEmbargoed">2016-04-18</date><ref type="file" target="https://hal.inria.fr/hal-01263456/document"><date notBefore="2016-04-18"></date></ref><ref type="file" subtype="author" n="1" target="https://hal.inria.fr/hal-01263456/file/arima00512.pdf"><date notBefore="2016-04-18"></date></ref></edition><respStmt><resp>contributor</resp><name key="201264"><persName><forename>Coordination</forename><surname>Episciences Iam</surname></persName><email>episciences@inria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-01263456</idno><idno type="halUri">https://hal.inria.fr/hal-01263456</idno><idno type="halBibtex">hamzaouil:hal-01263456</idno><idno type="halRefHtml">Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées, INRIA, 2006, 5, pp.158-167</idno><idno type="halRef">Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées, INRIA, 2006, 5, pp.158-167</idno><availability status="restricted"><licence target="http://creativecommons.org/licenses/by/">Attribution</licence></availability></publicationStmt><seriesStmt><idno type="stamp" n="UNIV-LEHAVRE">Université du Havre</idno><idno type="stamp" n="INSMI">CNRS-INSMI - INstitut des Sciences Mathématiques et de leurs Interactions</idno><idno type="stamp" n="ARIMA">ARIMA</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="fr">Segmentation d'une image couleur par les critères d'information et la théorie des ensembles flous</title><author role="aut"><persName><forename type="first">H.</forename><surname>Hamzaouil</surname></persName><idno type="halAuthorId">1282643</idno><affiliation ref="#struct-447007"></affiliation></author><author role="aut"><persName><forename type="first">A.</forename><surname>Elmatouat</surname></persName><idno type="halAuthorId">1282644</idno><affiliation ref="#struct-447168"></affiliation></author><author role="aut"><persName><forename type="first">P.</forename><surname>Martin</surname></persName><idno type="halAuthorId">847</idno><affiliation ref="#struct-447173"></affiliation></author></analytic><monogr><idno type="halJournalId" status="VALID">62852</idno><idno type="issn">1638-5713</idno><title level="j">Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées</title><imprint><publisher>INRIA</publisher><biblScope unit="volume">5</biblScope><biblScope unit="pp">158-167</biblScope><date type="datePub">2006</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="fr">French</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">Information Criteria</term><term xml:lang="en"> Histogram</term><term xml:lang="en"> Fuzzy Classification Algorithm</term><term xml:lang="en"> Fuzzy sets</term><term xml:lang="fr">Histogramme</term><term xml:lang="fr">Algorithme de classification floue</term><term xml:lang="fr">Ensembles flous</term><term xml:lang="fr">Critères d'information</term></keywords><classCode scheme="halDomain" n="math">Mathematics [math]</classCode><classCode scheme="halDomain" n="info">Computer Science [cs]</classCode><classCode scheme="halTypology" n="ART">Journal articles</classCode></textClass><abstract xml:lang="en">In this paper we present an unsupervised color image segmentation algorithm using the information criteria and a fuzzy theory. We propose this method to estimate the number of color image clusters and the optimal radius associated with minimizing the value of the proposed criteria. The experimental results demonstrate that this approach compresses the image in a small number of clusters without losing the informational contents of the image and we reduce the number of parameters using the process of segmentation, we also decrease the computational time. The color image segmentation system has been tested on some usual color images; "House", "Lena", "Monarch" and "Peppers".</abstract><abstract xml:lang="fr">Nous nous intéressons dans cet article à ladétermination du nombre de classes d'une image couleur par les critères d'information et la théorie des ensembles flous. Nous montrons que ces critères servent à estimer le nombre de régions d'une image couleur ainsi que le rayon optimal associé. Cette démarche est classée dans la catégorie des méthodes de segmentation non supervisée. Elle entraîne une compression de l'image en un nombre de couleurs représentatif sans perdre le contenu nformationnel de cette dernière. Elle réduit ainsi le nombre de paramètres considérés dans le systéme de la segmentation. Dans la dernière partie de cet article, nous montrons la performance et l'efficacité du nouvel algorithme en segmentant des images couleur tests: "House", "Lena", "Monarch" et "Peppers</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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