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A hybrid edge detection method based on fuzzy set theory and cellular learning automata

Sinaie, Saman and Ghanizadeh, Afshin and Majd, Emadaldin Mozafari and Shamsuddin, Siti Mariyam (2009) A hybrid edge detection method based on fuzzy set theory and cellular learning automata. In: Proceedings of the 2009 International Conference on Computational Science and Its Applications, ICCSA 2009. IEEE Computer Society, Washington, DC, USA, pp. 208-214. ISBN 978-0-7695-3701-6

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Official URL: http://dx.doi.org/10.1109/ICCSA.2009.19


In this paper, a hybrid edge detection method based on fuzzy sets and cellular learning automata is proposed. At first, existing methods of edge detection and their problems are discussed and then a high performance method for edge detection, that can extract edges more precisely by using only fuzzy sets than by other edge detection methods, is suggested. After that the edges improve incredibly by using cellular learning automata. In the end, we compare it with popular edge detection methods such as Sobel and Canny. The proposed method does not need parameter settings as Canny edge detector does, and it can detect edges more smoothly in a shorter amount of time while other edge detectors cannot.

Item Type:Book Section
Additional Information:Article no. 5260920
Subjects:H Social Sciences > HB Economic Theory
Divisions:Computer Science and Information System (Formerly known)
ID Code:11791
Deposited By: Nor Asmida Abdullah
Deposited On:17 Jan 2011 12:32
Last Modified:02 Oct 2017 04:51

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