A Novel Approach for Edge Detection using AntColony Otimization and Fuzz Derivative Technique

An approach involving a new ant colony optimization (ACO) and fuzzy derivative is presented to tackle the image edge detection problem. Ant colony optimization (ACO) is inspired from the foraging behavior of some ant species which deposit pheromone on their way. Ant colonies and more generally social insects act as a distributed system presenting a highly structured social organization. They communicate with each other by modifying the environment (stigmergy). The number of ants acting on the image is decided by the variation of fuzzy probability factor calculated from fuzzy derivatives which establishes a pheronone matrix. To avoid the movement of ants due to the variation of intensity caused by noise we use fuzzy derivative approach to make sure that the variation of intensity due to an edge is reflected in the probabilistic transition matrix. Finally a binary decision is made on the pheromone matrix by calculating a threshold adaptively.