Multi-circle detection on images using artificial bee colony (ABC) optimization

  • Authors:

  • Erik Cuevas;Felipe Sención-Echauri;Daniel Zaldivar;Marco Pérez-Cisneros

  • Affiliations:

  • Universidad de Guadalajara, CUCEI, Departamento de Ciencias Computacionales, Av. Revolución 1500, Guadalajara, Jal, Mexico;Universidad de Guadalajara, CUCEI, Departamento de Ciencias Computacionales, Av. Revolución 1500, Guadalajara, Jal, Mexico;Universidad de Guadalajara, CUCEI, Departamento de Ciencias Computacionales, Av. Revolución 1500, Guadalajara, Jal, Mexico;Universidad de Guadalajara, CUCEI, Departamento de Ciencias Computacionales, Av. Revolución 1500, Guadalajara, Jal, Mexico

  • Venue:
  • Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary Computation on General Purpose Graphics Processing Units
  • Year:
  • 2012

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Abstract

Hough transform has been the most common method for circle detection, exhibiting robustness, but adversely demanding considerable computational effort and large memory requirements. Alternative approaches include heuristic methods that employ iterative optimization procedures for detecting multiple circles. Since only one circle can be marked at each optimization cycle, multiple executions ought to be enforced in order to achieve multi-detection. This paper presents an algorithm for automatic detection of multiple circular shapes that considers the overall process as a multi-modal optimization problem. The approach is based on the artificial bee colony (ABC) algorithm, a swarm optimization algorithm inspired by the intelligent foraging behavior of honeybees. Unlike the original ABC algorithm, the proposed approach presents the addition of a memory for discarded solutions. Such memory allows holding important information regarding other local optima, which might have emerged during the optimization process. The detector uses a combination of three non-collinear edge points as parameters to determine circle candidates. A matching function (nectar-amount) determines if such circle candidates (bee-food sources) are actually present in the image. Guided by the values of such matching functions, the set of encoded candidate circles are evolved through the ABC algorithm so that the best candidate (global optimum) can be fitted into an actual circle within the edge-only image. Then, an analysis of the incorporated memory is executed in order to identify potential local optima, i.e., other circles. The proposed method is able to detect single or multiple circles from a digital image through only one optimization pass. Simulation results over several synthetic and natural images, with a varying range of complexity, validate the efficiency of the proposed technique regarding its accuracy, speed, and robustness.