TY - JOUR
T1 - Circle detection using electro-magnetism optimization
AU - Cuevas, Erik
AU - Oliva, Diego
AU - Zaldivar, Daniel
AU - Pérez-Cisneros, Marco
AU - Sossa, Humberto
N1 - Funding Information:
Humberto Sossa would like to thank SIP-IPN under Grant No. 20100468 for its economical support. The authors thank the European Union, the European Commission and CONACYT for the support. This paper has been prepared by an economical support of the European Commission under grant FONCICYT 93829 . The content of this paper is an exclusive responsibility of the UDEG and the CIC-IPN and it cannot be considered a reflection of the European Union position. The proposed algorithm is part of the vision system used by a biped robot supported under the grant CONACYT CB 82877.
PY - 2012/1/1
Y1 - 2012/1/1
N2 - 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.
AB - 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.
KW - Circle detection
KW - Collective intelligence
KW - Electro-magnetism optimization
KW - Intelligent image processing
KW - Nature-inspired algorithms
KW - Shape detection
UR - http://www.scopus.com/inward/record.url?scp=80055045787&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2010.12.024
DO - 10.1016/j.ins.2010.12.024
M3 - Artículo
SN - 0020-0255
VL - 182
SP - 40
EP - 55
JO - Information Sciences
JF - Information Sciences
IS - 1
ER -