Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
447526 | AEU - International Journal of Electronics and Communications | 2015 | 9 Pages |
Abstract
In this paper, we propose a cellular edge detection (CED) algorithm which utilizes cellular automata (CA) and cellular learning automata (CLA). The CED algorithm is an adaptive, intelligent and learnable algorithm for edge detection of binary and grayscale images. Here, we introduce a new CA local rule with adaptive neighborhood type to produce the edge map of image as opposed to CA with fixed neighborhood. The proposed adaptive algorithm uses the von Neumann and Moore neighborhood types. Experimental results demonstrate that the CED algorithm has superior accuracy and performance in contrast to other edge detection methods such as Sobel, Prewitt, Robert, LoG and Canny operators. Moreover, the CED algorithm loses fewer details while extracting image edges compare to other edge detection methods.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Networks and Communications
Authors
Mohammad Hasanzadeh Mofrad, Sana Sadeghi, Alireza Rezvanian, Mohammad Reza Meybodi,