Ant Colony Optimization based Edge Detection in Digital Images

Edge detection is identifying the discontinuities in the intensity of the pixel and grouping the contour of edges. Edge detection in digital images is a significant phenomenon in computer vision. In the initial stages of development of edge detection techniques, typical methods like Sobel Canny came into existence. These methods are fast but sensitive towards noise. Soft computing techniques like ant colony optimization (ACO), Genetic Algorithms (GA), Particle Swarm optimization (PSO) and Fuzzy Logic System (FLS) have extensive application in image edge detection due to their adaptiveness. In this paper, prominent ACO based techniques for edge detection are discussed and compared. The quality of edges identified by ACO based edge detection technique depends on the choice of constants, pheromone evaporation rate, number of iterations etc.