| dc.contributor | Cuevas, E., Departamento de Electrónica, Universidad de Guadalajara, CUCEI, Avenue Revolución 1500, CP 44430, Guadalajara, JAL, Mexico; Santuario, E.L., Departamento de Electrónica, Universidad de Guadalajara, CUCEI, Avenue Revolución 1500, CP 44430, Guadalajara, JAL, Mexico; Zaldívar, D., Departamento de Electrónica, Universidad de Guadalajara, CUCEI, Avenue Revolución 1500, CP 44430, Guadalajara, JAL, Mexico; Perez-Cisneros, M., Departamento de Ingenierías, Universidad de Guadalajara, CUTONALA, Morelos 180, CP 45400, Tonalá, JAL, Mexico | |
| dc.description.abstract | This paper presents an algorithm for the automatic detection of circular shapes from complicated and noisy images with no consideration of the conventional Hough transform principles. The proposed algorithm is based on a newly developed evolutionary algorithm called the Adaptive Population with Reduced Evaluations (APRE). Our proposed algorithm reduces the number of function evaluations through the use of two mechanisms: (1) adapting dynamically the size of the population and (2) incorporating a fitness calculation strategy, which decides whether the calculation or estimation of the new generated individuals is feasible. As a result, the approach can substantially reduce the number of function evaluations, yet preserving the good search capabilities of an evolutionary approach. Experimental results over several synthetic and natural images, with a varying range of complexity, validate the efficiency of the proposed technique with regard to accuracy, speed, and robustness. Zapotitlán 2013 Erik Cuevas et al. | |
