Circle detection using electro-magnetism optimization

TL;DR

This paper introduces a fast, accurate circle detection method using Electromagnetism-Like Optimization, which effectively locates circles in noisy images by modeling detection as an optimization problem.

Contribution

The paper presents a novel circle detection approach based on EMO, combining heuristic optimization with edge point analysis for improved accuracy and speed.

Findings

Achieves sub-pixel accuracy in circle detection

Effective in noisy and complex images

Faster than traditional methods

Abstract

This paper describes a circle detection method based on Electromagnetism-Like Optimization (EMO). Circle detection has received considerable attention over the last years thanks to its relevance for many computer vision tasks. EMO is a heuristic method for solving complex optimization problems inspired in electromagnetism principles. This algorithm searches a solution based in the attraction and repulsion among prototype candidates. In this paper the detection process is considered to be similar to an optimization problem, the algorithm uses the combination of three edge points (x, y, r) as parameters to determine circles candidates in the scene. An objective function determines if such circle candidates are actually present in the image. The EMO algorithm is used to find the circle candidate that is better related with the real circle present in the image according to the objective function. The final algorithm is a fast circle detector that locates circles with sub-pixel accuracy even considering complicated conditions and noisy images.