Advertisement

Journal of Intelligent & Robotic Systems

, Volume 66, Issue 3, pp 359–376 | Cite as

Circle Detection by Harmony Search Optimization

  • Erik Cuevas
  • Noé Ortega-Sánchez
  • Daniel ZaldivarEmail author
  • Marco Pérez-Cisneros
Article

Abstract

Automatic circle detection in digital images has received considerable attention over the last years in computer vision as several novel efforts aim for an optimal circle detector. This paper presents an algorithm for automatic detection of circular shapes considering the overall process as an optimization problem. The approach is based on the Harmony Search Algorithm (HSA), a derivative free meta-heuristic optimization algorithm inspired by musicians improvising new harmonies while playing. The algorithm uses the encoding of three points as candidate circles (harmonies) over the edge-only image. An objective function evaluates (harmony quality) if such candidate circles are actually present in the edge image. Guided by the values of this objective function, the set of encoded candidate circles are evolved using the HSA so that they can fit into the actual circles on the edge map of the image (optimal harmony). Experimental results from several tests on synthetic and natural images with a varying complexity range have been included to validate the efficiency of the proposed technique regarding accuracy, speed and robustness.

Keywords

Circle detection Harmony search algorithm Meta-heuristic algorithms Intelligent image processing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    da Fontoura Costa, L., Marcondes Cesar, R. Jr.: Shape Análisis and Classification. CRC, Boca Raton (2001)Google Scholar
  2. 2.
    Yuen, H., Princen, J., Illingworth, J., Kittler, J.: Comparative study of Hough transform methods for circle finding. Image Vis. Comput. 8(1), 71–77 (1990)CrossRefGoogle Scholar
  3. 3.
    Iivarinen, J., Peura, M., Sarela, J., Visa, A.: Comparison of combined shape descriptors for irregular objects. In: Proc. 8th British Machine Vision Conf., Cochester, UK, pp. 430–439 (1997)Google Scholar
  4. 4.
    Jones, G., Princen, J., Illingworth, J., Kittler, J.: Robust estimation of shape parameters. In: Proc. British Machine Vision Conf., pp. 43–48 (1990)Google Scholar
  5. 5.
    Fischer, M., Bolles, R.: Random sample consensus: a paradigm to model fitting with applications to image analysis and automated cartography. CACM 24(6), 381–395 (1981)Google Scholar
  6. 6.
    Bongiovanni, G., Crescenzi, P.: Parallel simulated annealing for shape detection. Comput. Vis. Image Underst. 61(1), 60–69 (1995)CrossRefGoogle Scholar
  7. 7.
    Roth, G., Levine, M.D.: Geometric primitive extraction using a genetic algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 16(9), 901–905 (1994)CrossRefGoogle Scholar
  8. 8.
    Peura, M., Iivarinen, J.: Efficiency of simple shape descriptors. In: Arcelli, C., Cordella, L.P., di Baja, G.S. (eds.) Advances in Visual Form Analysis, pp. 443–451. World Scientific, Singapore (1997)Google Scholar
  9. 9.
    Muammar, H., Nixon, M.: Approaches to extending the Hough transform. In: Proc. Int. Conf. on Acoustics, Speech and Signal Processing ICASSP_89, vol. 3, pp. 1556–1559 (1989)Google Scholar
  10. 10.
    Atherton, T.J., Kerbyson, D.J.: Using phase to represent radius in the coherent circle Hough transform. Proc, IEE Colloquium on the Hough Transform, IEE, London (1993)Google Scholar
  11. 11.
    Shaked, D., Yaron, O., Kiryati, N.: Deriving stopping rules for the probabilistic Hough transform by sequential analysis. Comput. Vis. Image Underst. 63, 512–526 (1996)CrossRefGoogle Scholar
  12. 12.
    Xu, L., Oja, E., Kultanen, P.: A new curve detection method: randomized Hough transform (RHT). Pattern Recogn. Lett. 11(5), 331–338 (1990)zbMATHCrossRefGoogle Scholar
  13. 13.
    Han, J.H., Koczy, L.T., Poston, T.: Fuzzy Hough transform. In: Proc. 2nd Int. Conf. on Fuzzy Systems, vol. 2, pp. 803–808 (1993)Google Scholar
  14. 14.
    Becker, J., Grousson, S., Coltuc, D.: From Hough transforms to integral transforms. In: Proc. Int. Geoscience and Remote Sensing Symp., 2002 IGARSS_02, vol. 3, pp. 1444–1446 (2002)Google Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    Dasgupta, S., Das, S., Biswas, A., Abraham, A.: Automatic circle detection on digital images whit an adaptive bacterial foraging algorithm. Soft Comput. 2009, 1151–1164 (2009). doi: 10.1007/s00500-009-0508-z Google Scholar
  17. 17.
    Cuevas, E., Zaldivar, D., Pérez-Cisneros, M., Ramírez-Ortegón, M.: Circle detection using discrete differential evolution optimization. Pattern Anal. Appl. 14, 93–107 (2010). doi: 10.1007/s10044-010-0183-9 CrossRefGoogle Scholar
  18. 18.
    Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulations 76, 60–68 (2001)CrossRefGoogle Scholar
  19. 19.
    Mahdavi, M., Fesanghary, M., Damangir, E.: An improved harmony search algorithm for solving optimization problems. Appl. Math. Comput. 188, 1567–1579 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  20. 20.
    Omran, M.G.H., Mahdavi, M.: Global-best harmony search. Appl. Math. Comput. 198, 643–656 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  21. 21.
    Lee, K.S., Geem, Z.W.: A new meta-heuristic algorithm for continuous engineering optimization, harmony search theory and practice. Comput. Methods Appl. Mech. Eng. 194, 3902–3933 (2005)zbMATHCrossRefGoogle Scholar
  22. 22.
    Lee, K.S., Geem, Z.W., Lee, S.H., Bae, K.-W.: The harmony search heuristic algorithm for discrete structural optimization. Eng. Optim. 37, 663–684 (2005)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Kim, J.H., Geem, Z.W., Kim, E.S.: Parameter estimation of the nonlinear Muskingum model using harmony search. J. Am. Water Resour. Assoc. 37, 1131–1138 (2001)CrossRefGoogle Scholar
  24. 24.
    Geem, Z.W.: Optimal cost design of water distribution networks using harmony search. Eng. Optim. 38, 259–280 (2006)CrossRefGoogle Scholar
  25. 25.
    Lee, K.S., Geem, Z.W.: A new structural optimization method based on the harmony search algorithm. Comput. Struct. 82, 781–798 (2004)CrossRefGoogle Scholar
  26. 26.
    Ayvaz, T.M.: Simultaneous determination of aquifer parameters and zone structures with fuzzy c-means clustering and meta-heuristic harmony search algorithm. Adv. Water Resour. 30, 2326–2338 (2007)CrossRefGoogle Scholar
  27. 27.
    Geem, Z.W., Lee, K.S., Park, Y.J.: Application of harmony search to vehicle routing. Am. J. Appl. Sci. 2, 1552–1557 (2005)CrossRefGoogle Scholar
  28. 28.
    Geem, Z.W.: Novel derivative of harmony search algorithm for discrete design variables. Appl. Math. Comput. 199(1), 223–230 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  29. 29.
    Vasebi, A., Fesanghary, M., Bathaee, S.M.T.: Combined heat and power economic dispatch by harmony search algorithm. Electr. Power Energy Syst. 29, 713–719 (2007)CrossRefGoogle Scholar
  30. 30.
    Geem, Z.W.: Harmony search optimization to the pump-included water distribution network design. Civ. Eng. Environ. Syst. 26(3), 211–221 (2009)CrossRefGoogle Scholar
  31. 31.
    Geem, Z.W.: Particle-swarm harmony search for water network design. Eng. Optim. 41(4), 297–311 (2009)CrossRefGoogle Scholar
  32. 32.
    Geem, Z.W., Kim, J., Loganathan, G.: Harmony search optimization: application to pipe network design. Int. J. Model Simul. 22(2), 125–133 (2002)Google Scholar
  33. 33.
    Degertekin, S.O.: Optimum design of steel frames using harmony search algorithm. Struct. Multidiscipl. Optim. 36(4), 393–401 (2008)CrossRefGoogle Scholar
  34. 34.
    Forsati, R., Haghighat, A.T., Mahdavi, M.: Harmony search based algorithms for bandwidth-delay-constrained least-cost multicast routing. Comput. Commun. 31(10), 2505–2519 (2008)CrossRefGoogle Scholar
  35. 35.
    Ceylan, H., Ceylan, H., HaIdenbilen, S., et al.: Transport energy modeling with meta-heuristic harmony search algorithm, an application to Turkey. Energy Policy 36(7), 2527–2535 (2008)CrossRefGoogle Scholar
  36. 36.
    Fesanghary, M., Mahdavi, M., Minary-Jolandan, M., et al.: Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems. Comput. Methods Appl. Mech. Eng. 197(33–40), 3080–3091 (2008)zbMATHCrossRefGoogle Scholar
  37. 37.
    Kaveha, A., Talataharib, S.: Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures. Comput. Struct. 87(5–6), 267–283 (2009)CrossRefGoogle Scholar
  38. 38.
    Mun, S., Geem, Z.W.: Determination of individual sound power levels of noise sources using a harmony search algorithm. Int. J. Ind. Ergon. 39(2), 366–370 (2009)CrossRefGoogle Scholar
  39. 39.
    Mun, S., Geem, Z.W.: Determination of viscoelastic and damage properties of hot mix asphalt concrete using a harmony search algorithm. Mech. Mater. 41(3), 339–353 (2009)CrossRefGoogle Scholar
  40. 40.
    Pan, Q.-K., Suganthan, P.N., Liang, J.J., Fatih Tasgetiren, M.: A local-best harmony search algorithm with dynamic sub-harmony memories for lot-streaming flow shop scheduling problem. Expert Syst. Appl. 38, 3252–3259 (2011)CrossRefGoogle Scholar
  41. 41.
    Pan, Q.-K., Suganthan, P.N., Fatih Tasgetiren, M., Liang, J.J.: A self-adaptive global best harmony search algorithm for continuous optimization problems. Appl. Math. Comput. 216, 830–848 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  42. 42.
    Bresenham, J.E.: A linear algorithm for incremental digital display of circular arcs. Commun. ACM 20, 100–106 (1987)CrossRefGoogle Scholar
  43. 43.
    Van Aken, J.R.: Efficient ellipse-drawing algorithm. IEEE Comp. Graphics Appl. 4(9), 24–35 (2005)CrossRefGoogle Scholar
  44. 44.
    Kelly, M., Levine, M.: Finding and describing objects in complex images: advances in image understanding. IEEE Computer Society Press, pp. 209–225 (1997)Google Scholar
  45. 45.
    Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics 1, 80–83 (1945)CrossRefGoogle Scholar
  46. 46.
    Garcia, S., Molina, D., Lozano, M., Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special session on real parameter optimization. J. Heurist. (2008). doi: 10.1007/s10732-008-9080-4 Google Scholar
  47. 47.
    Santamaría, J., Cordón, O., Damas, S., García-Torres, J.M., Quirin, A.: Performance evaluation of memetic approaches in 3D reconstruction of forensic objects. Soft Comput. 13(8), 883–904 (2009). doi: 10.1007/s00500-008-0351-7 CrossRefGoogle Scholar
  48. 48.
    Chen, T.-C., Chung, K.-L.: An eficient randomized algorithm for detecting circles. Comput. Vis. Image Underst. 83, 172–191 (2001)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Erik Cuevas
    • 1
  • Noé Ortega-Sánchez
    • 1
  • Daniel Zaldivar
    • 1
    Email author
  • Marco Pérez-Cisneros
    • 1
  1. 1.Departamento de Ciencias ComputacionalesUniversidad de Guadalajara, CUCEIGuadalajaraMéxico

Personalised recommendations