  Hough transform (HT) has been the most common method for circle detection
exhibiting robustness but adversely demanding a considerable computational load
and large storage. Alternative approaches include heuristic methods that employ
iterative optimization procedures for detecting multiple circles under the
inconvenience that only one circle can be marked at each optimization cycle
demanding a longer execution time. On the other hand, Learning Automata (LA) is
a heuristic method to solve complex multi-modal optimization problems. Although
LA converges to just one global minimum, the final probability distribution
holds valuable information regarding other local minima which have emerged
during the optimization process. The detection process is considered as a
multi-modal optimization problem, allowing the detection of multiple circular
shapes through only one optimization procedure. The algorithm uses a
combination of three edge points as parameters to determine circles candidates.
A reinforcement signal determines if such circle candidates are actually
present at the image. Guided by the values of such reinforcement signal, the
set of encoded candidate circles are evolved using the LA so that they can fit
into actual circular shapes over the edge-only map of the image. The overall
approach is a fast multiple-circle detector despite facing complicated
conditions.
