Ant colony optimization for image edge detection

Authors:
Anna Veronica Baterina;Carlos Oppus
Affiliations:
Department of Electronics, Computer, and Communications Engineering, Ateneo de Manila University, Quezon City, Philippines;Department of Electronics, Computer, and Communications Engineering, Ateneo de Manila University, Quezon City, Philippines
Venue:
ISPRA'10 Proceedings of the 9th WSEAS international conference on Signal processing, robotics and automation
Year:
2010

Citing 3
Cited 0

Ant Colony Optimization

Ant Colony Optimization
Edge detection using ant algorithms

Soft Computing - A Fusion of Foundations, Methodologies and Applications
Ant system: optimization by a colony of cooperating agents

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

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Abstract

Ant colony optimization is a population-based metaheuristic that mimics the foraging behavior of ants to find approximate solutions to difficult optimization problems. This paper presents an ACO-based technique for image edge detection. The proposed method establishes a pheromone matrix that represents the edge information at each pixel based on the routes formed by ants dispatched on the image. The movement of the ants is guided by the local variation of the image's intensity values. The proposed ACO-based approach takes advantage of the improvements introduced in ant colony system, one of the main extensions to the original ant system. Experimental results show the success of the technique in extracting edges from a digital image.