  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.
