Fuzzy edge detector using entropy optimization

Hanmandlu, M. and See, J. and Vasikarla, S. (2004) Fuzzy edge detector using entropy optimization. In: ITCC 2004. International Conference on Information Technology: Coding and Computing, 2004. Proceedings. IEEE Xplore, pp. 665-670. ISBN 0-7695-2108-8

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Abstract

This 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 fh and the crossover point xc, is used to enhance the image. The entropy function is optimized to obtain the parameters fh, and xc 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, α and β. 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.

Item Type: Book Section
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 20 Dec 2013 02:11
Last Modified: 20 Dec 2013 02:11
URI: http://shdl.mmu.edu.my/id/eprint/4622

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