Training Cellular Automata with Extended Neighborhood for Edge Detection

dc.contributor.author	Safia, Djemame
dc.date.accessioned	2024-03-12T19:05:49Z
dc.date.available	2024-03-12T19:05:49Z
dc.date.issued	2021-05-25
dc.description.abstract	Edge detection refers to the process of identifying and locating sharp discontinuities in an image. Since edge detection is in the forefront of image processing for object detection, it has attracted much attention from scientific research. More accurate results and less time consuming are there still the main issues when extracting edges from images. To cope with this challenge, we propose a complex system: Cellular Automata (CA) that has proven high performances in image processing domain. Unlike previous works, which used in majority Von Neumann or Moore neighborhood, We use a particular kind of CA, with extended Moore neighborhood. This allows a large exploration of the search space. We trained a QPSO algorithm for extracting the adequante subset of rules. Experiments were carried on several images from Mathworks and Berkeley dataset. Visual and numerical results show that our CA provides excellent performances, and edges with high accuracy.
dc.identifier.isbn	978-9931-9788-0-0
dc.identifier.uri	http://dspace.univ-oeb.dz:4000/handle/123456789/18736
dc.language.iso	en
dc.publisher	University of Oum El Bouaghi
dc.subject	Artificial intelligence; complex systems; cellular automata; rule selection; image processing; edge detection.
dc.title	Training Cellular Automata with Extended Neighborhood for Edge Detection
dc.type	Article

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