Circle Detection Algorithm Based on Electromagnetism-Like Optimization

  • Erik Cuevas
  • Diego Oliva
  • Daniel Zaldivar
  • Marco Pérez
  • Raúl Rojas
Part of the Intelligent Systems Reference Library book series (ISRL, volume 38)


Optimization approaches, inspired by different metaphors, have recently attracted the interest of the scientist community. On the other hand, circle detection over digital images has received considerable attention from the computer vision community over the last few years as tremendous efforts have been directed towards seeking for an optimal detector. This chapter presents an algorithm for the automatic detection of circular shapes embedded into cluttered and noisy images with no consideration of conventional Hough transform techniques. The approach is based on a physics-inspired technique known as the Electromagnetism-like Optimization (EMO). It follows the Electromagnetism principle regarding a 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 the edge map as candidate circles. 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 actual circular shapes over the edge map. 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.


Particle Swarm Optimization Local Search Synthetic Image Pepper Noise Rosenbrock Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Andalóa, F.A., Miranda, P.A.V., Torres, R.S., Falcão, A.X.: Shape feature extraction and description based on tensor scale. Pattern Recognition 43, 26–36 (2010)CrossRefGoogle Scholar
  2. 2.
    Andrei, N.: Acceleration of conjugate gradient algorithms for unconstrained optimization. Applied Mathematics and Computation 213, 361–369 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Atherton, T.J., Kerbyson, D.J.: Using phase to represent radius in the coherent circle Hough transform. In: Proc., IEE Colloquium on the Hough Transform. IEE, London (1993)Google Scholar
  4. 4.
    Ayala-Ramirez, V., Garcia-Capulin, C.H., Perez-Garcia, A., Sanchez-Yanez, R.E.: Circle detection on images using genetic algorithms. Pattern Recognition Letters 27, 652–657 (2006)CrossRefGoogle Scholar
  5. 5.
    Baia, X., Yangb, X., Jan-Latecki, L.: Detection and recognition of contour parts based on shape similarity. Pattern Recognition 41, 2189–2199 (2008)CrossRefGoogle Scholar
  6. 6.
    Becker, J., Grousson, S., Coltuc, D.: From Hough transforms to integral transforms. In: Proceedings Int. Geoscience and Remote Sensing Symp., 2002 IGARSS 2002, pp. 1444–1446 (2002)Google Scholar
  7. 7.
    Blum, C.: Ant colony optimization: Introduction and recent trends. Physics of Life Reviews 2, 353–373 (2005)CrossRefGoogle Scholar
  8. 8.
    Bongiovanni, G., Crescenzi, P.: Parallel Simulated Annealing for Shape Detection. Computer Vision and Image Understanding 61, 60–69 (1995)CrossRefGoogle Scholar
  9. 9.
    Bresenham, J.E.: A Linear Algorithm for Incremental Digital Display of Circular Arcs. Communications of the ACM 20, 100–106 (1977)zbMATHCrossRefGoogle Scholar
  10. 10.
    Chak, U.K.: Genetic and evolutionary computing. Information Sciences 178, 4419–4420 (2008)CrossRefGoogle Scholar
  11. 11.
    Dua, W., Li, B.: Multi-strategy ensemble particle swarm optimization for dynamic optimization. Information Sciences 178, 3096–3109 (2008)CrossRefGoogle Scholar
  12. 12.
    Fischer, M., Bolles, R.: Random sample consensus: A paradigm to model fitting with applications to image analysis and automated cartography. CACM 24, 381–395 (1981)Google Scholar
  13. 13.
    Graña, M.: Evolutionary algorithms. Information Sciences 133, 101–102 (2001)zbMATHCrossRefGoogle Scholar
  14. 14.
    Gruber, T.: Collective knowledge systems: Where the Social Web meets the Semantic Web. Web Semantics: Science. Services and Agents on the World Wide Web 6, 4–13 (2008)CrossRefGoogle Scholar
  15. 15.
    Han, J.H., Koczy, L.T., Poston, T.: Fuzzy Hough transform. In: Proc. 2nd Int. Conf. on Fuzzy Systems, pp. 803–808 (1993)Google Scholar
  16. 16.
    Hongwei, M.: Handbook of Research on Artificial Immune Systems and Natural Computing: Applying Complex Adaptive Technologies. IGI Global (2009)Google Scholar
  17. 17.
    İlker, B., Birbil, S., Shu-Cherng, F.: An Electromagnetism-like Mechanism for Global Optimization. Journal of Global Optimization 25, 263–282 (2003)zbMATHCrossRefGoogle Scholar
  18. 18.
    İlker, B., Birbil, S., Shu-Cherng, F., Sheu, R.L.: On the convergence of a population-based global optimization algorithm. Journal of Global Optimization 30, 301–318 (2004)zbMATHCrossRefGoogle Scholar
  19. 19.
    Jhen-Yan, J., Kun-Chou, L.: Array pattern optimization using electromagnetism-like algorithm. AEU - International Journal of Electronics and Communications 63, 491–496 (2009)CrossRefGoogle Scholar
  20. 20.
    Karaboga, D., Akay, B.: A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and Computation 214, 108–132 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  21. 21.
    Lee, C.H., Chang, F.K.: Fractional-order PID controller optimization via improved electromagnetism-like algorithm. Expert Systems with Applications 37, 8871–8878 (2010)CrossRefGoogle Scholar
  22. 22.
    Lina, Y.H., Chen, C.H.: Template matching using the parametric template vector with translation, rotation and scale invariance. Pattern Recognition 41, 2413–2421 (2008)CrossRefGoogle Scholar
  23. 23.
    Liu, J., Tsui, K.: Toward nature-inspired computing. Commun. ACM 49, 59–64 (2006)CrossRefGoogle Scholar
  24. 24.
    Loia, V.: Soft computing meets agents. Information Sciences 176, 1101–1102 (2006)CrossRefGoogle Scholar
  25. 25.
    Lutton, E., Martinez, P.: A genetic algorithm for the detection 2-D geometric primitives on images. In: Proc. of the 12th Int. Conf. on Pattern Recognition, pp. 526–528 (1994)Google Scholar
  26. 26.
    Lévy, P.: From social computing to reflexive collective intelligence: The IEML research program. Information Sciences 180, 71–94 (2010)CrossRefGoogle Scholar
  27. 27.
    Martín, J.A., Santos, M., de Lope, J.: Orthogonal variant moments features in image analysis. Information Sciences 180, 846–860 (2010)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Moliton, A.: Baisc Electromagnetism and Materials. Springer (2007)Google Scholar
  29. 29.
    Muammar, H., Nixon, M.: Approaches to extending the Hough transform. In: Proc. Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP 1989, pp. 1556–1559 (1989)Google Scholar
  30. 30.
    Naji-Azimi, Z., Toth, P., Galli, L.: An electromagnetism metaheuristic for the unicost set covering problem. European Journal of Operational Research 205, 290–300 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  31. 31.
    Naderi, B., Tavakkoli-Moghaddam, R., Khalili, M.: Electromagnetism-like mechanism and simulated annealing algorithms for flowshop scheduling problems minimizing the total weighted tardiness and makespan. Knowledge-Based Systems 23, 77–85 (2010)CrossRefGoogle Scholar
  32. 32.
    Qia, H., Lia, K., Shena, Y., Qu, W.: An effective solution for trademark image retrieval by combining shape description and feature matching. Pattern Recognition 43, 2017–2027 (2010)CrossRefGoogle Scholar
  33. 33.
    Rocha, A., Fernandes, E.: Hybridizing the electromagnetism-like algorithm with descent search for solving engineering design problems. International Journal of Computer Mathematics 86, 1932–1946 (2009)zbMATHCrossRefGoogle Scholar
  34. 34.
    Rocha, A., Fernandes, E.: Modified movement force vector in an electromagnetism-like mechanism for global optimization. Optimization Methods & Software 24, 253–270 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  35. 35.
    Rosin, P.L.: Further five point fit ellipse fitting. In: Proc. 8th British Machine Vision Conf., Cochester, UK, pp. 290–299 (1997)Google Scholar
  36. 36.
    Rosin, P.L., Nyongesa, H.O.: Combining evolutionary, connectionist, and fuzzy classification algorithms for shape analysis. In: Cagnoni, S., et al. (eds.) Proc. EvoIASP, Real-World Applications of Evolutionary Computing, pp. 87–96 (2000)Google Scholar
  37. 37.
    Roth, G., Levine, M.D.: Geometric primitive extraction using a genetic algorithm. IEEE Trans. Pattern Anal. Machine Intell. 16, 901–905 (1994)CrossRefGoogle Scholar
  38. 38.
    Schindlera, K., Suterb, D.: Object detection by global contour shape. Pattern Recognition 41, 3736–3748 (2008)CrossRefGoogle Scholar
  39. 39.
    Schut, M.C.: On model design for simulation of collective intelligence. Information Sciences 180, 132–155 (2010)CrossRefGoogle Scholar
  40. 40.
    Shaked, D., Yaron, O., Kiryati, N.: Deriving stopping rules for the probabilistic Hough transform by sequential analysis. Comput. Vision Image Understanding 63, 512–526 (1996)CrossRefGoogle Scholar
  41. 41.
    Teodorović, D.: Swarm intelligence systems for transportation engineering: Principles and applications. Transportation Research Part C: Emerging Technologies 16, 651–667 (2008)CrossRefGoogle Scholar
  42. 42.
    Tiana, J., Yub, W., Ma, L.: AntShrink: Ant colony optimization for image shrinkage. Pattern Recognition Letters 31, 1751–1758 (2010)CrossRefGoogle Scholar
  43. 43.
    Tsou, C.S., Kao, C.H.: Multi-objective inventory control using electromagnetism-like metaheuristic. International Journal of Production Research 46, 3859–3874 (2008)zbMATHCrossRefGoogle Scholar
  44. 44.
    Van-Aken, J.R.: An Efficient Ellipse Drawing Algorithm. CG&A 4, 24–35 (1984)Google Scholar
  45. 45.
    Wu, P., Wen-Hung, Y., Nai-Chieh, W.: An electromagnetism algorithm of neural network analysis an application to textile retail operation. Journal of the Chinese Institute of Industrial Engineers 21, 59–67 (2004)CrossRefGoogle Scholar
  46. 46.
    Xu, L., Oja, E., Kultanen, P.: A new curve detection method: Randomized Hough transform (RHT). Pattern Recognition Lett. 11, 331–338 (1990)zbMATHCrossRefGoogle Scholar
  47. 47.
    Yao, J., Kharma, N., Grogono, P.: Fast robust GA-based ellipse detection. In: Proc. 17th Int. Conf. on Pattern Recognition, ICPR 2004, Cambridge, UK, pp. 859–862 (2004)Google Scholar
  48. 48.
    Yin, H., Huang, W.: Adaptive nonlinear manifolds and their applications to pattern recognition. Information Sciences 180, 2649–2662 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  49. 49.
    Ying-ping, C., Pei, J.: Analysis of particle interaction in particle swarm optimization. Theoretical Computer Science 411, 2101–2115 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  50. 50.
    Yuen, H., Princen, J., Illingworth, J., Kittler, J.: Comparative study of Hough transform methods for circle finding. Image Vision Comput. 8, 71–77 (1990)CrossRefGoogle Scholar
  51. 51.
    Yuen, S., Ma, C.: Genetic algorithm with competitive image labelling and least square. Pattern Recognition 33, 1949–1966 (2000)zbMATHCrossRefGoogle Scholar
  52. 52.
    Yurtkuran, A., Emel, E.: A new Hybrid Electromagnetism-like Algorithm for capacitated vehicle routing problems. Expert Systems with Applications 37, 3427–3433 (2010)CrossRefGoogle Scholar
  53. 53.
    Zhang, Q., Mahfouf, M.: A nature-inspired multi-objective optimization strategy based on a new reduced space search ing algorithm for the design of alloy steels. Engineering Applications of Artificial Intelligence (2010), doi:10.1016/j.engappai.2010.01.017Google Scholar
  54. 54.
    Zhang, X., Rosin, P.L.: Superellipse fitting to partial data. Pattern Recognition 36, 743–752 (2003)zbMATHCrossRefGoogle Scholar
  55. 55.
    Dixon, L.C.W., Szegö, G.P.: The global optimization problem: An introduction. In: Towards Global Optimization 2, pp. 1–15. North-Holland, Amsterdam (1978)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Erik Cuevas
    • 1
  • Diego Oliva
    • 1
  • Daniel Zaldivar
    • 1
  • Marco Pérez
    • 1
  • Raúl Rojas
    • 2
  1. 1.Departamento de Ciencias ComputacionalesUniversidad de Guadalajara, CUCEIGuadalajaraMéxico
  2. 2.Institut für InformatikFreie Universität BerlinBerlinGermany

Personalised recommendations