Skip to main content

Circle Detection Algorithm Based on Electromagnetism-Like Optimization

  • Chapter
Handbook of Optimization

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 38))

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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)

    Article  Google Scholar 

  2. Andrei, N.: Acceleration of conjugate gradient algorithms for unconstrained optimization. Applied Mathematics and Computation 213, 361–369 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  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. 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)

    Article  Google Scholar 

  5. Baia, X., Yangb, X., Jan-Latecki, L.: Detection and recognition of contour parts based on shape similarity. Pattern Recognition 41, 2189–2199 (2008)

    Article  Google Scholar 

  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. Blum, C.: Ant colony optimization: Introduction and recent trends. Physics of Life Reviews 2, 353–373 (2005)

    Article  Google Scholar 

  8. Bongiovanni, G., Crescenzi, P.: Parallel Simulated Annealing for Shape Detection. Computer Vision and Image Understanding 61, 60–69 (1995)

    Article  Google Scholar 

  9. Bresenham, J.E.: A Linear Algorithm for Incremental Digital Display of Circular Arcs. Communications of the ACM 20, 100–106 (1977)

    Article  MATH  Google Scholar 

  10. Chak, U.K.: Genetic and evolutionary computing. Information Sciences 178, 4419–4420 (2008)

    Article  Google Scholar 

  11. Dua, W., Li, B.: Multi-strategy ensemble particle swarm optimization for dynamic optimization. Information Sciences 178, 3096–3109 (2008)

    Article  Google Scholar 

  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. Graña, M.: Evolutionary algorithms. Information Sciences 133, 101–102 (2001)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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. Hongwei, M.: Handbook of Research on Artificial Immune Systems and Natural Computing: Applying Complex Adaptive Technologies. IGI Global (2009)

    Google Scholar 

  17. İlker, B., Birbil, S., Shu-Cherng, F.: An Electromagnetism-like Mechanism for Global Optimization. Journal of Global Optimization 25, 263–282 (2003)

    Article  MATH  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  20. Karaboga, D., Akay, B.: A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and Computation 214, 108–132 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  23. Liu, J., Tsui, K.: Toward nature-inspired computing. Commun. ACM 49, 59–64 (2006)

    Article  Google Scholar 

  24. Loia, V.: Soft computing meets agents. Information Sciences 176, 1101–1102 (2006)

    Article  Google Scholar 

  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. Lévy, P.: From social computing to reflexive collective intelligence: The IEML research program. Information Sciences 180, 71–94 (2010)

    Article  Google Scholar 

  27. Martín, J.A., Santos, M., de Lope, J.: Orthogonal variant moments features in image analysis. Information Sciences 180, 846–860 (2010)

    Article  MathSciNet  Google Scholar 

  28. Moliton, A.: Baisc Electromagnetism and Materials. Springer (2007)

    Google Scholar 

  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. 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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  34. Rocha, A., Fernandes, E.: Modified movement force vector in an electromagnetism-like mechanism for global optimization. Optimization Methods & Software 24, 253–270 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  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. 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. Roth, G., Levine, M.D.: Geometric primitive extraction using a genetic algorithm. IEEE Trans. Pattern Anal. Machine Intell. 16, 901–905 (1994)

    Article  Google Scholar 

  38. Schindlera, K., Suterb, D.: Object detection by global contour shape. Pattern Recognition 41, 3736–3748 (2008)

    Article  Google Scholar 

  39. Schut, M.C.: On model design for simulation of collective intelligence. Information Sciences 180, 132–155 (2010)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  41. Teodorović, D.: Swarm intelligence systems for transportation engineering: Principles and applications. Transportation Research Part C: Emerging Technologies 16, 651–667 (2008)

    Article  Google Scholar 

  42. Tiana, J., Yub, W., Ma, L.: AntShrink: Ant colony optimization for image shrinkage. Pattern Recognition Letters 31, 1751–1758 (2010)

    Article  Google Scholar 

  43. Tsou, C.S., Kao, C.H.: Multi-objective inventory control using electromagnetism-like metaheuristic. International Journal of Production Research 46, 3859–3874 (2008)

    Article  MATH  Google Scholar 

  44. Van-Aken, J.R.: An Efficient Ellipse Drawing Algorithm. CG&A 4, 24–35 (1984)

    Google Scholar 

  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)

    Article  Google Scholar 

  46. Xu, L., Oja, E., Kultanen, P.: A new curve detection method: Randomized Hough transform (RHT). Pattern Recognition Lett. 11, 331–338 (1990)

    Article  MATH  Google Scholar 

  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. Yin, H., Huang, W.: Adaptive nonlinear manifolds and their applications to pattern recognition. Information Sciences 180, 2649–2662 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  49. Ying-ping, C., Pei, J.: Analysis of particle interaction in particle swarm optimization. Theoretical Computer Science 411, 2101–2115 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  51. Yuen, S., Ma, C.: Genetic algorithm with competitive image labelling and least square. Pattern Recognition 33, 1949–1966 (2000)

    Article  MATH  Google Scholar 

  52. Yurtkuran, A., Emel, E.: A new Hybrid Electromagnetism-like Algorithm for capacitated vehicle routing problems. Expert Systems with Applications 37, 3427–3433 (2010)

    Article  Google Scholar 

  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.2010.01.017

    Google Scholar 

  54. Zhang, X., Rosin, P.L.: Superellipse fitting to partial data. Pattern Recognition 36, 743–752 (2003)

    Article  MATH  Google Scholar 

  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 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Erik Cuevas .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Cuevas, E., Oliva, D., Zaldivar, D., Pérez, M., Rojas, R. (2013). Circle Detection Algorithm Based on Electromagnetism-Like Optimization. In: Zelinka, I., Snášel, V., Abraham, A. (eds) Handbook of Optimization. Intelligent Systems Reference Library, vol 38. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30504-7_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-30504-7_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30503-0

  • Online ISBN: 978-3-642-30504-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics