Automatic Circle Detection on Images with Annealed Differential Evolution

This article presents an algorithm for the automatic detection of circular shapes from complicated and noisy images. The algorithm is based on a hybrid technique composed of simulated annealing and differential evolution. A new fuzzy objective function has been derived for the edge map of a given image. Minimization of this function with a hybrid annealed differential evolution algorithm leads to the automatic detection of circles on the image. Simulation results over several synthetic as well as natural images with varying range of complexity validate the efficacy of the proposed technique in terms of its final accuracy, speed and robustness.