Abstract
Object and shape detection in digital image were one of the hot topic over the last two decades. Especially automatic multi circle detection has received more attention over last years. Hough transform (HT) is a well-known and most popular method for lines and circles detection. However, HT has huge computational complexity expense. This paper proposed a new successful heuristic method to reduce computation time and improve the speed of HT for circle detection. In this proposed method the edges information of the image is obtained by means of Robert edge detection. Then, multimodal particle swarm optimization (PSO) and local search is employed to locate all exciting circle in the image. The experiments on benchmark images show that our scheme can perform multi circle detection successfully.
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References
Cuevas, E., Oliva, D., Zaldivar, D., Pérez-Cisneros, M., Sossa, H.: Circle detection using electro-magnetism optimization. Inf. Sci. 182, 40–55 (2012)
Yuen, H., Princen, J., Illingworth, J., Kittler, J.: Comparative study of Hough transform methods for circle finding. Image Vis. Comput. 8, 71–77 (1990)
Shapiro, L., Stockman, G.: Computer vision. In: Chapter-5, Filtering and Enhancing Images. Prentice-Hall, Inc., New Jersey (2001)
Duda, R.O., Hart, P.E.: Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM 15, 11–15 (1972)
Cheng, H.-D., Guo, Y., Zhang, Y.: A novel Hough transform based on eliminating particle swarm optimization and its applications. Pattern Recogn. 42, 1959–1969 (2009)
Rahkar-Farshi, T., Kesemen, O., Behjat-Jamal, S.: Multi hyperbole detection on images using modified artificial bee colony (ABC) for multimodal function optimization. In: Proceedings of 2014 22nd Signal Processing and Communications Applications Conference (SIU), 23–25 Apr 2014, pp. 894–898 (2014)
Ayala-Ramirez, V., Garcia-Capulin, C.H., Perez-Garcia, A., Sanchez-Yanez, R.E.: Circle detection on images using genetic algorithms. Pattern Recogn. Lett. 27, 652–657 (2006)
Zhang, S., Zhou, X., Wang, Y., Gao, J., Wang, H.: Circle detection based on artificial bee colony algorithm. Sci. Bull. Natl. Min. Univ. (2016)
Rahkar Farshi, T.: Battle royale optimization algorithm. Neural Comput. Appl. 33, 1139–1157 (2021)
Orujpour, M., Feizi-Derakhshi, M.-R., Rahkar-Farshi, T.: Multi-modal forest optimization algorithm. Neural Comput. Appl. (2019). https://doi.org/10.1007/s00521-019-04113-z
Farshi, T.R., Drake, J.H., Özcan, E.: A multimodal particle swarm optimization-based approach for image segmentation. Expert Syst. Appl. 149, 113233 (2020). https://doi.org/10.1016/j.eswa.2020.113233
Rahkar Farshi, T., Orujpour, M.: A multi-modal bacterial foraging optimization algorithm. J. Ambient Intell. Humaniz. Comput. 12, 10035–10049 (2021)
Farshi, T.R.: A memetic animal migration optimizer for multimodal optimization. Evol. Syst. 13, 133–144 (2022)
Grüninger, T., Wallace, D.: Multimodal optimization using genetic algorithms. Master’s thesis, Stuttgart University (1996)
Ursem, R.K.: Multinational GAs: multimodal optimization techniques in dynamic environments. In: Proceedings of the 2nd Annual Conference on Genetic and Evolutionary Computation, pp. 19–26
Wei, L., Zhao, M.: A niche hybrid genetic algorithm for global optimization of continuous multimodal functions. Appl. Math. Comput. 160, 649–661 (2005)
Dilettoso, E., Salerno, N.: A self-adaptive niching genetic algorithm for multimodal optimization of electromagnetic devices. IEEE Trans. Magn. 42, 1203–1206 (2006)
Li, X.: A multimodal particle swarm optimizer based on fitness Euclidean-distance ratio. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 78–85
Barrera, J., Coello, C.A.C.: A particle swarm optimization method for multimodal optimization based on electrostatic interaction. In: Proceedings of Mexican International Conference on Artificial Intelligence, pp. 622–632
Qu, B.-Y., Suganthan, P.N., Das, S.: A distance-based locally informed particle swarm model for multimodal optimization. IEEE Trans. Evol. Comput. 17, 387–402 (2013)
Rahkar-Farshi, T., Behjat-Jamal, S., Feizi-Derakhshi, M.-R.: An improved multimodal PSO method based on electrostatic interaction using n-nearest-neighbor local search (2014). arXiv preprint arXiv:1410.2056
Cuevas, E., Sención-Echauri, F., Zaldivar, D., Pérez-Cisneros, M.: Multi-circle detection on images using artificial bee colony (ABC) optimization. Soft Comput. 16, 281–296 (2012)
Qu, B.-Y., Liang, J.J., Suganthan, P.N.: Niching particle swarm optimization with local search for multi-modal optimization. Inf. Sci. 197, 131–143 (2012)
Hu, X., Eberhart, R.C., Shi, Y.: Particle swarm with extended memory for multiobjective optimization. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium, SIS’03, pp. 193–197 (2003)
Zhang, J., Zhang, J.-R., Li, K.: A sequential niching technique for particle swarm optimization. In: Proceedings of International Conference on Intelligent Computing, pp. 390–399
Li, X.: Adaptively choosing neighbourhood bests using species in a particle swarm optimizer for multimodal function optimization. In: Proceedings of Genetic and Evolutionary Computation Conference, pp. 105–116
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AC drafted the manuscript, proofread the manuscript and approved the final manuscript. SM drafted the manuscript. TA provided core concepts, drafted the manuscript, carried out implementations and simulations for this manuscript.
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Cetin, A., Rezai, S., Akan, T. (2023). Multi-circle Detection Using Multimodal Optimization. In: Akan, T., Anter, A.M., Etaner-Uyar, A.Ş., Oliva, D. (eds) Engineering Applications of Modern Metaheuristics. Studies in Computational Intelligence, vol 1069. Springer, Cham. https://doi.org/10.1007/978-3-031-16832-1_11
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DOI: https://doi.org/10.1007/978-3-031-16832-1_11
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