  Circle detection over digital images has received considerable attention from
the computer vision community over the last few years devoting a tremendous
amount of research seeking for an optimal detector. This article presents an
algorithm for the automatic detection of circular shapes from complicated and
noisy images with no consideration of conventional Hough transform principles.
The proposed algorithm is based on Learning Automata (LA) which is a
probabilistic optimization method that explores an unknown random environment
by progressively improving the performance via a reinforcement signal
(objective function). The approach uses the encoding of three non-collinear
points as a candidate circle over the edge image. A reinforcement signal
(matching function) indicates if such candidate circles are actually present in
the edge map. Guided by the values of such reinforcement signal, the
probability set of the encoded candidate circles is modified through the LA
algorithm so that they can fit to the actual circles on the edge map.
Experimental results over several complex synthetic and natural images have
validated the efficiency of the proposed technique regarding accuracy, speed
and robustness.
