Improved Image Thresholding Using Ant Colony Optimization Algorithm

Abstract

The Ant colony optimization (ACO) algorithm is relatively a new meta-heuristic algorithm and a successful paradigm of all the algorithms which take advantage of the insectpsilas behavior. It has been applied to solve many optimization problems with good discretion, parallel, robustness and positive feedback. As an advanced optimization algorithm, only recently, researchers began to apply ACO to image processing tasks. In this paper, an Improved Image Thresholding Method using Ant Colony Optimization Algorithm is proposed. Compared with traditional thresholding segmentation methods, the proposed method has advantages that it can nicely segment the thin, it can efficiently reduce calculation time, and it has good capability and stabilization nature. The results show that using the proposed method can achieve satisfactory segmentation effect.