Automatic circle detection on images using the Teaching Learning Based Optimization algorithm and gradient analysis

Circle extraction is usually a previous task used in different applications related to biometrics, robotics, medical image analysis among others. Solutions based on meta-heuristic approaches, such as evolutionary and swarm-based algorithms, have been adopted in order to overcome the main deficiencies of Hough Transform methods. In this paper, the task of circle detection is presented as an optimization problem, where each circle represents an optimum within the feasible search space. To this end, a circle detection method is proposed based on the Teaching Learning Based Optimization algorithm, which is a population-based technique that is inspired by the teaching and learning processes. Additionally, improvements to the evolutionary approach for circle detection are obtained by exploiting gradient information for the construction of the search space and the definition of the objective function. To validate the efficacy of the proposed circle detector, several tests using noisy and complex images as input were carried out, and the results compared with different approaches for circle detection.