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Please use this identifier to cite or link to this item: http://eprint.iitd.ac.in/handle/2074/2229

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dc.contributor.authorHanmandlu, Madasu-
dc.contributor.authorSee, John-
dc.contributor.authorVasikarla, Shantaram-
dc.identifier.citationInformation Technology: Coding and Computing, Proceedings ITCC International Conference on, 1, 665 - 670p.en
dc.description.abstractThis paper proposes a fuzzy-based approach to edge detection in gray-level images. The proposed fuzzy edge detector involves two phases - global contrast intensification and local fuzzy edge detection. In the first phase, a modified Gaussian membership function is chosen to represent each pixel in the fuzzy plane. A global contrast intensification operator, containing three parameters, viz., intensification parameter t, fuzzifier f/sub h/ and the crossover point x/sub c/, is used to enhance the image. The entropy function is optimized to obtain the parameters f/sub h/, and x/sub c/ using the gradient descent function before applying the local edge operator in the second phase. The local edge operator is a generalized Gaussian function containing two exponential parameters, /spl alpha/ and /spl beta/. These parameters are obtained by the similar entropy optimization method. By using the proposed technique, a marked visible improvement in the important edges is observed on various test images over common edge detectors.en
dc.format.extent370656 bytes-
dc.subjectEdge detectoren
dc.subjectFuzzy image processingen
dc.subjectImage enhancementen
dc.subjectContrast intensification operatoren
dc.subjectCrossover pointen
dc.subjectGaussian membership functionen
dc.titleFuzzy edge detector using entropy optimizationen
Appears in Collections:Electrical Engineering

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