Please use this identifier to cite or link to this item: http://148.72.244.84/xmlui/handle/xmlui/12577
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dc.contributor.authorZaki .S. Towfik-
dc.date.accessioned2024-03-14T06:00:48Z-
dc.date.available2024-03-14T06:00:48Z-
dc.date.issued2010-
dc.identifier.issn2222-8373-
dc.identifier.urihttp://148.72.244.84:8080/xmlui/handle/xmlui/12577-
dc.description.abstractThis paper compare between the traditional fuzzy C-Means clustering FCM and a proposed technique approach to geometrically guided fuzzy clustering. A modified fuzzy CMeans clustering (FCM), is extended to incorporate a priori geometrical information from spatial domain in order to improve image segmentation. This leads to a new algorithm where the cluster guidance is determined by the membership values on neighboring pixels. The algorithm of FCM is tested on synthetic and real image to demonstrate the improved image segmentation compared to traditional FCM.en_US
dc.language.isoenen_US
dc.subjectfuzzy C-means, Geometrically guided condition Fuzzy C-Means clustering, Conditional Fuzzy C-mean clustering, Fuzzy neighborhood, Edge preserving threshold, Defuzzificationen_US
dc.titleComparison Between fuzzy C-Means Clustering (FCM) and geometrically guided condition Fuzzy C-Means clustering (ggc FCM)en_US
dc.typeArticleen_US
Appears in Collections:مجلة ديالى للعلوم الاكاديمية / Academic Science Journal (Acad. Sci. J.)

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