A convenient and robust edge detection method based on ant colony optimization

Abstract Edge detection is usually used as a preprocessing operation in many machine vision industrial applications. Recently, ant colony optimization (ACO) as a relatively new meta-heuristic approach has been used to tackle the edge detection problem. In this work, a convenient and robust method for edge detection based on ACO is proposed, which employs a new heuristic function, adopts a user-defined threshold in pheromone update process and provides a group of suitable parameter values. Experimental results clearly demonstrated the effectiveness of the proposed method, and at the same time, in the presence of noise, the proposed approach outperforms other two ACO-based edge detection techniques and four conventional edge detectors.

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