  Automatic circle detection in digital images has received considerable
attention over the last years in computer vision as several efforts have aimed
for an optimal circle detector. This paper presents an algorithm for automatic
detection of circular shapes that considers the overall process as an
optimization problem. The approach is based on the Harmony Search Algorithm
(HSA), a derivative free meta-heuristic optimization algorithm inspired by
musicians while improvising new harmonies. The algorithm uses the encoding of
three points as candidate circles (harmonies) over the edge-only image. An
objective function evaluates (harmony quality) if such candidate circles are
actually present in the edge image. Guided by the values of this objective
function, the set of encoded candidate circles are evolved using the HSA so
that they can fit to the actual circles on the edge map of the image (optimal
harmony). Experimental results from several tests on synthetic and natural
images with a varying complexity range have been included to validate the
efficiency of the proposed technique regarding accuracy, speed and robustness.
