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            <idno type="halRefHtml">&lt;i&gt;Revue Africaine de Recherche en Informatique et Mathématiques Appliquées&lt;/i&gt;, 2006, Volume 5, Special Issue TAM TAM'05, november 2006, pp.158-167. &lt;a target="_blank" href="https://dx.doi.org/10.46298/arima.1861"&gt;&amp;#x27E8;10.46298/arima.1861&amp;#x27E9;&lt;/a&gt;</idno>
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                <term xml:lang="en"> Fuzzy sets</term>
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              <p>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</p>
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