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Automatic multi-circle detection on images using the teaching learning based optimisation algorithm

Circle detection has numerous applications towards industry, robotics, and science in general. Therefore, a significant effort has been made in order to develop an accurate and fast method for circle extraction. Commonly, different techniques such as the ones based on the Hough transform have been widely used because of their robustness. However, these techniques usually demand a considerable computational load and large storage, and therefore meta-heuristic approaches such as evolutionary and swarm-based algorithms have been studied as an alternative. This study introduces a circle-detection method based on a recently proposed meta-heuristic technique: the teaching learning based optimisation algorithm, which is a population-based technique that is inspired by the teaching and learning processes. The algorithm uses the encoding of three points as candidate circles over the edge image. To evaluate if such candidate circles are actually present within the edge map, an objective function is used to guide the search. To validate the efficacy of the proposed approach, several tests using noisy and complex images as input were carried out, and the results were compared with different approaches for circle detection.