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Ant colony optimization for image edge detection

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.