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A color constancy method using fuzzy measures and integrals

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The ability of measuring colors of objects, independent of light source illumination, is called color constancy which is an important problem in machine vision and image processing fields. In this paper, we propose a new combinational method that is based on fuzzy measures and integrals to estimate the chromaticity of the light source as the major step of color constancy. The basic idea of the proposed method is that there are color constancy methods with some similarities in their structure and the way they are applied. The proposed method works with the help of assigning fuzzy measures to these methods and their combinations and computing the Choquet fuzzy integral. To approve the proposed method, we selected four well known algorithms and their results were combined by the proposed approach. In selecting these methods, it was tried to choose the ones which had better performance in compare to other methods, however the proposed method can be applied on any other methods just by adjusting its parameters. It is shown in this article that proposed approach performs better than other proposed methods for color constancy most of the time.

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  1. 1)

    D. Forsyth: Int. J. Comput. Vision 5 (1990) 5.

    Article  Google Scholar 

  2. 2)

    M. Ebner: Color Constancy (Wiley, New York, 2007) Wiley IS&T Series in Imaging Science and Technology, 1st ed.

    Google Scholar 

  3. 3)

    S. D. Hordley: Color Res. Appl. 31 (2006) 303.

    Article  Google Scholar 

  4. 4)

    S. Bianco, G. Ciocca, C. Cusano, and R. Schettini: Pattern Recognition 43 (2010) 695.

    MATH  Article  Google Scholar 

  5. 5)

    E. Land and J. McCann: J. Opt. Soc. Am. 61 (1971) 1.

    ADS  Article  Google Scholar 

  6. 6)

    G. Buchsbaum: J. Franklin Inst. 310 (1980) 1.

    Article  Google Scholar 

  7. 7)

    G. Finlayson and E. Trezzi: Proc. IS&T/SID 12th Color Imaging Conf., 2004, p. 37.

  8. 8)

    G. Finlayson and S. Hordley: Int. J. Comput. Vision 67 (2006) 93.

    Article  Google Scholar 

  9. 9)

    B. Funt, V. Cardei, and K. Barnard: Proc. IS&T/SID Forth Color Imaging Conf., 1996.

  10. 10)

    M. Ebner: Pattern Recognition Lett. 27 (2006) 1220.

    Article  Google Scholar 

  11. 11)

    J. van de Weijer, T. Gevers, and A. Gijsenij: IEEE Trans. Image Process. 16 (2007) 2207.

    MathSciNet  ADS  Article  Google Scholar 

  12. 12)

    M. Ebner: Mach. Vision Appl. 20 (2009) 283.

    Article  Google Scholar 

  13. 13)

    A. Gijsenij, T. Gevers, and J. Weijer: Int. J. Comput. Vision 86 (2010) 127.

    Article  Google Scholar 

  14. 14)

    S. Bianco, G. Ciocca, C. Cusano, and R. Schettini: IEEE Trans. Image Process. 17 (2008) 2381.

    MathSciNet  ADS  Article  Google Scholar 

  15. 15)

    J. Weijer, C. Schmid, and J. Verbeek: IEEE 11th Int. Conf. Computer Vision, 2007.

  16. 16)

    A. Gijsenij and T. Gevers: IEEE Trans. Pattern Anal. Mach. Intell. 33 (2010) 687.

    Article  Google Scholar 

  17. 17)

    V. C. Cardei and B. Funt: Proc. IS&T/SID 7th Color Imaging Conf.: Color Science, Systems and Applications, 1999.

  18. 18)

    K. Barnard, L. Martin, A. Coath, and B. Funt: IEEE Trans. Image Process. 11 (2002) 985.

    ADS  Article  Google Scholar 

  19. 19)

    T. Akhavan and M. Ebrahimi Moghadam: Int. Conf. Image Processing Theory, Tools and Applications, 2010.

  20. 20)

    M. Bertalmío, V. Caselles, and E. Provenzi: Int. J. Comput. Vision 83 (2009) 101.

    Article  Google Scholar 

  21. 21)

    E. H. Land: Am. Sci. 52 (1964) 247.

    Google Scholar 

  22. 22)

    M. Grabisch, T. Murofushi, and M. Sugeno: Fuzzy Measures and Integrals: Theory and Applications (Springer, New York, 2000) p. 70.

    MATH  Google Scholar 

  23. 23)

    R. Mesiar: J. Fuzzy Sets Syst. 156 (2005) 365.

    MathSciNet  MATH  Article  Google Scholar 

  24. 24)

  25. 25)

    F. Ciurea and B. Funt: in Proc. IS&T/SID 11th Color Imaging Conf., 2003, p. 160.

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Akhavan, T., Moghaddam, M.E. A color constancy method using fuzzy measures and integrals. OPT REV 18, 273–283 (2011).

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  • color constancy
  • RGB color space
  • fuzzy measures
  • Choquet fuzzy integral