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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2074/2246
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| DC Field | Value | Language |
|---|
| contributor.author | Hanmandlu, Madasu | - |
| contributor.author | Madasu, Vamsi Krishna | - |
| contributor.author | Vasikarla, Shantaram | - |
| date.accessioned | 2006-10-12T11:01:48Z | - |
| date.available | 2006-10-12T11:01:48Z | - |
| date.issued | 2004 | - |
| identifier.citation | Information Technology: Coding and Computing, Proceedings ITCC International Conference on, 1, 636 - 642p. | en |
| identifier.uri | http://eprint.iitd.ac.in/dspace/handle/2074/2246 | - |
| description.abstract | The 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 |
| format.extent | 63620 bytes | - |
| format.mimetype | application/msword | - |
| language.iso | en | en |
| subject | Texture | en |
| subject | Fractal dimension | en |
| subject | Modified mountain clustering | en |
| subject | Potential | en |
| subject | Validity | en |
| subject | Segmentation | en |
| title | A fuzzy approach to texture segmentation | en |
| type | Article | en |
| Appears in Collections: | Electrical Engineering
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Files in This Item:
| File |
Description |
Size | Format |
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| Hanmandlufuz2004.pdf | | 1470Kb | Adobe PDF | View/Open |
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