Modified artificial bee colony algorithms for solving multiple circle detection problem

Determining circular shapes from digital images is one of the most required operations in computer vision and its applications. Various techniques including evolutionary and swarm intelligence-based algorithms have been introduced and successfully used over the past decade for detecting single or multiple circles. In this study, two different circle candidate list generation approaches that are based on generating a combination between abandoned food sources and the obtained food sources in the final colony have been proposed and integrated into the workflow of the standard implementation of the artificial bee colony (ABC) algorithm. Experimental studies on a set of real and synthetic images showed that the proposed list generation approaches improved the solving capabilities of ABC algorithm and decreased the total error values related to the discovered circles compared to the ABC-based implementation for which a circle or circles are selected from a candidate list containing only abandoned food sources, genetic algorithm, bacterial foraging optimization algorithm and an improved version of the Hough transform-based circle detection technique called randomized Hough transform.