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Application of a Hybrid Ant Colony Optimization for the Multilevel Thresholding in Image Processing

  • Yun-Chia Liang
  • Angela Hsiang-Ling Chen
  • Chiuh-Cheng Chyu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)

Abstract

Our study proposes a hybrid optimization scheme based on an ant colony optimization algorithm with the Otsu method to render the optimal thresholding technique more applicable and effective. The properties of discriminate analysis in Otsu’s method are to analyze the separability among the gray levels in the image. The ACO-Otsu algorithm, a non-parametric and unsupervised method, is the first-known application of ACO to automatic threshold selection for image segmentation. The experimental results show that the ACO-Otsu efficiently speed up the Otsu’s method to a great extent at multi-level thresholding, and that such method can provide better effectiveness at population size of 20 for all given image types at multi-level thresholding in this study.

Keywords

Optimal Threshold State Transition Probability Image Thresholding Otsu Method Multilevel Thresholding 
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.

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References

  1. 1.
    Abutaleb, A.S.: Automatic thresholding of gray-level pictures using two-dimensional entropy. Comput. Vis. Graph. Image Process. 47, 22–32 (1989)CrossRefGoogle Scholar
  2. 2.
    Belkasim, S., Ghazal, A., Basir, O.A.: Phase-based optimal image thresholding. Digital Signal Process 13, 636–655 (2003)CrossRefGoogle Scholar
  3. 3.
    Cao, L., Shi, Z.K., Cheng, E.K.W.: Fast automatic multilevel thresholding method. Electron. Lett. 38, 868–870 (2002)CrossRefGoogle Scholar
  4. 4.
    Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugenics 7, 179–188 (1936)Google Scholar
  5. 5.
    Jian, X., Mojon, D.: Adaptive local thresholding by verification-based multithresholding probing with application to vessel detection in retinal images. IEEE Trans. Pattern. Anal. Machine Intell. 25, 131–137 (2003)CrossRefGoogle Scholar
  6. 6.
    Kapur, J.N., Sahoo, P.K., Wong, A.K.C.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vis. Graph Image Process. 29, 273–285 (1985)CrossRefGoogle Scholar
  7. 7.
    Kittler, J., Illingworth, J.: Minimum error thresholding. Pattern Recogn. 19, 41–47 (1986)CrossRefGoogle Scholar
  8. 8.
    Li, C.H., Lee, C.K.: Minimum cross entropy thresholding. Pattern Recogn. 26, 617–625 (1993)CrossRefGoogle Scholar
  9. 9.
    Liao, P.S., Chen, T.S., Chung, P.C.: A fast algorithm for multilevel thresholding. J. Inf. Sci. and Engineering 17, 713–727 (2001)Google Scholar
  10. 10.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979)CrossRefGoogle Scholar
  11. 11.
    Rosin, P.L., Ioannidis, E.: Evaluation of global image thresholding for change detection. Pattern Recogn. Lett. 24, 2345–2356 (2003)MATHCrossRefGoogle Scholar
  12. 12.
    Tsai, D.M.: A fast thresholding selection procedure for multimodal and unimodal histograms. Pattern Recogn. Lett. 16, 653–666 (1995)CrossRefGoogle Scholar
  13. 13.
    Wang, Q., Chi, Z., Zhao, R.: Image thresholding by maximizing of nonfusiness of the 2D grayscale histogram. Comput. Image and Vis. Understanding 85, 100–116 (2002)MATHCrossRefGoogle Scholar
  14. 14.
    Yan, F., Zhang, H., Kube, C.R.: A multistage adaptive thresholding method. Pattern Recogn. Lett. 26, 1183–1191 (2005)CrossRefGoogle Scholar
  15. 15.
    Yin, P.-Y.: A fast scheme for optimal thresholding using genetic algorithms. Signal Process 72, 85–95 (1999)MATHCrossRefGoogle Scholar
  16. 16.
    Yin, P.-Y., Chen, L.-H.: New method for multilevel thresholding using the symmetry a duality of the histogram. J. Electron. Imag. 2, 337–344 (1993)CrossRefGoogle Scholar
  17. 17.
    Yin, P.-Y., Chen, L.-H.: A fast iterative scheme for multi-level thresholding methods. Signal Process. 60, 305–313 (1997)MATHCrossRefGoogle Scholar
  18. 18.
    Zahara, E.: A Study of Nelder-Mead Simplex Search Method for Solving Unconstrained and Stochastic Optimization Problems. Ph.D. Dissertation, Yuan Ze University, Taiwan (2003)Google Scholar
  19. 19.
    Zahara, E., Fan, S.-K.S., Tsai, D.M.: Optimal multi-thresholding using a hybrid optimization approach. Pattern Recogn. Lett. 26, 1082–1095 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yun-Chia Liang
    • 1
  • Angela Hsiang-Ling Chen
    • 2
  • Chiuh-Cheng Chyu
    • 1
  1. 1.Department of Industrial Engineering and ManagementYuan Ze UniversityChung-Li, Taoyuan CountyTaiwan, R.O.C.
  2. 2.Department of Financial ManagementNanya Institute of TechnologyChung-Li, Taoyuan CountyTaiwan, R.O.C.

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