Analysis and Synthesis of an Ant Colony Optimization Technique for Image Edge Detection

Ant Colony optimization (ACO) is the technique which is used for solving computational problems and finding the best paths through graphs. ACO is based on the behavior of ants seeking paths from their colony to their food. Ants move randomly and after getting their food return back to their colony while laying down pheromone trails. Other ants find such a path and follow trail for returning. Pheromones are used for antâs communication. This technique is used for optimization in many applications like edge detection, network packet routing, structure health monitoring, vehicular routing, image segmentation traveling salesman problem, quadratic assignment problem, sequential ordering, scheduling, graph coloring, management of communications networks, image compression etc. In this paper we are using a method using ACO to find edge detection. It gives a pheromone matrix and memory stored positions that are followed by leading ant. The memory based positions are stored on the basis of intensity values with reference with a threshold value. The results are shown which successfully detect the edges of the image.