Sinaie, S. and Ghanizadeh, A. and Shamsuddin, S. M. and Majd , E. M. 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
Full text not available from this repository.
Official URL: http://dx.doi.org/10.1109/ICCSA.2009.19
n 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)|
|Deposited By:||Nor Asmida Abdullah|
|Deposited On:||17 Jan 2011 12:32|
|Last Modified:||17 Jan 2011 12:32|
Repository Staff Only: item control page