Authors: Fazel Keshtkar; Wail Gueaieb; Mehedi Masud
Addresses: School of Electrical Engineering and Computer Science (EECS), University of Ottawa, 800 King Edward Avenue, Ottawa, K1N 6N5, Ontario, Canada. ' School of Electrical Engineering and Computer Science (EECS), University of Ottawa, 800 King Edward Avenue, Ottawa, K1N 6N5, Ontario, Canada. ' Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21974, P.O. Box 888, Saudi Arabia
Abstract: Intelligent techniques such as swarm intelligence techniques rarely have been used for image segmentation or boundary detection. The limited increasing number of agents in the environment and how to find efficiently the right threshold in an image, develop a flexible design, and fully autonomous system that supports different platforms makes the task challenging. Considering challenges this paper presents a swarm-based intelligent technique for image segmentation that is based on a fully agent-based model system, called swarm intelligence-based image segmentation (SIBIS). SIBIS adopts a cellular automata technique where the swarm of agents navigate through the image and operate on their pixels and local regions. Three features such as swarm intelligence, agent-based modelling and cellular automata are integrated to make SIBIS efficient. SIBIS system can find the image segmentation threshold automatically without changing the background or the texture of the image.
Keywords: swarm intelligence; image processing; agent-based modelling; multi-agent systems; agent-based systems; cellular automaton; knowledge-based image segmentation; cellular automata.
International Journal of Innovative Computing and Applications, 2012 Vol.4 No.2, pp.75 - 91
Available online: 07 May 2012 *Full-text access for editors Access for subscribers Purchase this article Comment on this article