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A Fast Histogram Estimation Based on the Monte Carlo Method for Image Binarization

  • Piotr Lech
  • Krzysztof Okarma
  • Mateusz Tecław
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 233)

Summary

In the paper the idea of fast histogram estimation is proposed which is based on the application of the Monte Carlo method. Presented method can be useful for fast image binarization especially for low computational efficiency solutions e.g. autonomous mobile robots. Proposed method has been compared with full image analysis and the obtained estimates have been used for threshold determination and binarization using well-known Otsu method.

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References

  1. 1.
    Okarma, K., Lech, P.: A statistical reduced-reference approach to digital image quality assessment. In: Bolc, L., Kulikowski, J.L., Wojciechowski, K. (eds.) ICCVG 2008. LNCS, vol. 5337, pp. 43–54. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Okarma, K., Lech, P.: A fast image analysis technique for the line tracking robots. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010, Part II. LNCS (LNAI), vol. 6114, pp. 329–336. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  3. 3.
    Okarma, K., Lech, P.: Monte Carlo based algorithm for fast preliminary video analysis. In: Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2008, Part I. LNCS, vol. 5101, pp. 790–799. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Lima, J.B., Campello de Souza, R.M.: Histogram uniformization for digital image encryption. In: Proc. 25th SIBGRAPI Conf. Graphics, Patterns and Images, pp. 55–62 (2012)Google Scholar
  5. 5.
    Imamura, K., Kuroda, H., Fujimura, M.: Image content detection method using correlation coefficient between pixel value histograms. In: Kim, T.-H., Adeli, H., Ramos, C., Kang, B.-H. (eds.) SIP 2011. CCIS, vol. 260, pp. 1–9. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  6. 6.
    Forczmański, P., Kukharev, G., Shchegoleva, N.: An algorithm of face recognition under difficult lighting conditions. Electrical Review 88(10b), 201–204 (2012)Google Scholar
  7. 7.
    Otsu, A.: Threshold selection method from gray-level histograms. IEEE Trans. Systems, Man, and Cybernetics 9(1), 62–66 (1979)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering, Department of Signal Processing and Multimedia EngineeringWest Pomeranian University of Technology, SzczecinSzczecinPoland

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