Ingeniería, investigación y tecnología
versión impresa ISSN 1405-7743
The Physic-inspired computation is becoming popular and has been acknowledged by the scientific community. This emerging area has developed a wide range of techniques and methods for dealing with complex problems. On the other hand, automatic circle detection in digital images has been considered as 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 article presents an algorithm for the automatic detection of circular shapes embedded into complicated and noisy images with no consideration of the conventional Hough transform techniques. The approach is based on a nature-inspired technique called the Electromagnetism-Like Optimization (EMO) which is a heuristic method following electromagnetism principles for solving complex optimization problems. For the EMO algorithm, solutions are built considering the electromagnetic attraction and repulsion among charged particles with a charge representing the fitness solution for each particle. The algorithm uses the encoding of three non-collinear points as candidate circles over an edge-only image. Guided by the values of the objective function, the set of encoded candidate circles (charged particles) are evolved using the EMO algorithm so that they can fit into the actual circles on the edge map of the image. Experimental results from several tests on synthetic and natural images with a varying range of complexity are included to validate the efficiency of the proposed technique regarding accuracy, speed, and robustness.
Palabras llave : circle detection; image processing; Electromagnetism-Like Optimization.