An optimal edge detection using universal law of gravity and ant colony algorithm

An approach for edge detection using ant colony optimization (ACO) and universal law of gravity is presented in this paper. The direct application of the edge detector operator in an image requires a huge search space, therefore the task of edge detection is time consuming and memory exhausting without optimization. Ant colony optimization is an optimization algorithm inspired by the natural behavior of ant species that ants deposit pheromone on the ground for foraging. Ant colonies and more generally social insects act as a distributed system presenting a highly structured social organization. In the proposed approach the heuristic function is calculated using law of universal gravity which acts as the way to a food source for the artificial ants to detect the edge pixels.