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Please use this identifier to cite or link to this item: http://eprint.iitd.ac.in/handle/2074/2246

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dc.contributor.authorHanmandlu, Madasu-
dc.contributor.authorMadasu, Vamsi Krishna-
dc.contributor.authorVasikarla, Shantaram-
dc.date.accessioned2006-10-12T11:01:48Z-
dc.date.available2006-10-12T11:01:48Z-
dc.date.issued2004-
dc.identifier.citationInformation Technology: Coding and Computing, Proceedings ITCC International Conference on, 1, 636 - 642p.en
dc.identifier.urihttp://eprint.iitd.ac.in/dspace/handle/2074/2246-
dc.description.abstractThe texture segmentation techniques are diversified by the existence of several approaches. In this paper, we propose fuzzy features for the segmentation of texture image. For this purpose, a membership function is constructed to represent the effect of the neighboring pixels on the current pixel in a window. Using these membership function values, we find a feature by weighted average method for the current pixel. This is repeated for all pixels in the window treating each time one pixel as the current pixel. Using these fuzzy based features, we derive three descriptors such as maximum, entropy, and energy for each window. To segment the texture image, the modified mountain clustering that is unsupervised and fuzzy c-means clustering have been used. The performance of the proposed features is compared with that of fractal features.en
dc.format.extent63620 bytes-
dc.format.mimetypeapplication/msword-
dc.language.isoenen
dc.subjectTextureen
dc.subjectFractal dimensionen
dc.subjectModified mountain clusteringen
dc.subjectPotentialen
dc.subjectValidityen
dc.subjectSegmentationen
dc.titleA fuzzy approach to texture segmentationen
dc.typeArticleen
Appears in Collections:Electrical Engineering

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