Ant colony optimization (ACO) is a population-based metaheuristic that mimics the foraging behavior of ants to find approximate solutions to difficult optimization problems. It can be used to find good solutions to combinatorial optimization problems that can be transformed into the problem of finding good paths through a weighted construction graph. In this paper, an edge detection technique that is based on ACO is presented. The proposed method establishes a pheromone matrix that represents the edge information at each pixel based on the routes formed by the ants dispatched on the image. The movement of the ants is guided by the local variation in the image’s intensity values. The proposed ACObased edge detection method 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.
Baterina, A., & Oppus, C. (2010). Image Edge Detection Using Ant Colony Optimization. International Journal of Circuits, Systems and Signal Processing 6, 58-67.