Image Edge Detection with Fuzzy Ant Colony Optimization Algorithm

Abstract

Searching and optimizing by using collective intelligence are known as highly efficient methods that can be used to solve complex engineering problems. Ant colony optimization algorithm (ACO) is based on collective intelligence that is inspired by ants' behavior in finding the best path as searching for food. In this paper, the ACO algorithm is used for image edge detection. A fuzzy-based system is proposed to increase the dynamics and speed of the proposed method. This system controls the amount of pheromone and distance. Thus, instead of considering constant values for the parameters of the algorithm, variable values are used to make the search space more accurate and reasonable. The fuzzy ant colony optimization algorithm is applied on several images to illustrate the performance of the proposed algorithm. The obtained results show better quality in extracting edge pixels by the proposed method compared to several image edge detection methods. The improvement of the proposed method is shown quantitatively by the investigation of the time and the entropy of the conventional methods and previous works. Also, the robustness of the proposed method is demonstrated against additive noise.