Circle detection using electro-magnetism optimization

  • Authors:

  • Erik Cuevas;Diego Oliva;Daniel Zaldivar;Marco Pérez-Cisneros;Humberto Sossa

  • Affiliations:

  • Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal, Mexico;Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal, Mexico;Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal, Mexico;Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal, Mexico;Centro de Investigación en Computación, IPN, Av. Juan de Dios Bátiz s/n, Mexico, DF, Mexico

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2012

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Abstract

Nature-inspired computing has yielded remarkable applications of collective intelligence which considers simple elements for solving complex tasks by common interaction. On the other hand, automatic circle detection in digital images has been considered an important and complex task for the computer vision community that has devoted a tremendous amount of research, seeking for an optimal circle detector. This paper presents an algorithm for the automatic detection of circular shapes embedded into cluttered and noisy images without considering conventional Hough transform techniques. The approach is based on a nature-inspired technique known as the Electro-magnetism Optimization (EMO). It follows the electro-magnetism principle regarding a collective attraction-repulsion mechanism which manages particles towards an optimal solution. Each particle represents a solution by holding a charge which is related to the objective function to be optimized. The algorithm uses the encoding of three non-collinear points embedded into an edge-only image as candidate circles. Guided by the values of the objective function, the set of encoded candidate circles (charged particles) are evolved using an EMO algorithm so that they can fit into actual circular shapes over the edge-only map of the image. Experimental evidence from several tests on synthetic and natural images which provide a varying range of complexity validates the efficiency of our approach regarding accuracy, speed and robustness.