Circle detection using electro-magnetism optimization

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

Research output: Contribution to journalArticle

76 Citations (Scopus)

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. © 2011 Elsevier Inc. All rights reserved.
Original languageAmerican English
Pages (from-to)40-55
Number of pages34
JournalInformation Sciences
DOIs
StatePublished - 1 Jan 2012

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Electromagnetism
Magnetism
Circle
Optimization
Hough transforms
Objective function
Charged particles
Collective Intelligence
Computer vision
Hough Transform
Digital Image
Computer Vision
Detectors
Optimization Algorithm
Encoding
Optimal Solution
Detector
Charge
Robustness
Computing

Cite this

Cuevas, Erik ; Oliva, Diego ; Zaldivar, Daniel ; Pérez-Cisneros, Marco ; Sossa, Humberto. / Circle detection using electro-magnetism optimization. In: Information Sciences. 2012 ; pp. 40-55.
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Circle detection using electro-magnetism optimization. / Cuevas, Erik; Oliva, Diego; Zaldivar, Daniel; Pérez-Cisneros, Marco; Sossa, Humberto.

In: Information Sciences, 01.01.2012, p. 40-55.

Research output: Contribution to journalArticle

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