Illumination Chromaticity Estimation Based on Dichromatic Reflection Model and Imperfect Segmentation

  • Johji Tajima
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5646)


The illumination chromaticity estimation based on the dichromatic reflection model has not been made practicable, since the method needs image segmentation beforehand. However, its two-dimensional model is sufficiently robust, when it is combined with the least square method. The proposed algorithm executes the color space division instead of the segmentation. The original image is divided into small color regions, each of which corresponds to one of color sub-spaces. Though this division is imperfect image segmentation, the illumination chromaticity estimation based on the chromaticity distribution in the color regions is possible. Experimental result shows that this method is also applicable to images of apparently matt surfaces.


Illumination color estimation Dichromatic reflection model Color space division 


  1. 1.
    Finlayson, G.D., Hubel, P.M., Hordley, S.: Color by Correlation. In: Proc. 5th Color Imaging Conference, pp. 6–11 (1997)Google Scholar
  2. 2.
    Barnard, K., Cardei, V., Funt, B.: A Comparison of Computational Color Constancy Algorithms – Part I & II. IEEE Trans. on Image Processing 1(9), 972–996 (2002)CrossRefGoogle Scholar
  3. 3.
    Funt, B., Xiong, W.: Estimating Illumination Chromaticity via Support Vector Regression. In: Proc. 12th Color Imaging Conference, pp. 47–52 (2004)Google Scholar
  4. 4.
    Shafer, S.A.: Using Color to Separate Reflection Components. Color Res. Appl. 10, 210–218 (1985)CrossRefGoogle Scholar
  5. 5.
    Tominaga, S.: Surface Identification Using the Dichromatic Reflection Model. IEEE Trans. PAMI-13, 658–670 (1991)Google Scholar
  6. 6.
    Saito, T.: Method and Apparatus for Illumination Color Measurement of a Color Image. Japanese Patent 2081885 (1988) (in Japanese) Google Scholar
  7. 7.
    Tominaga, S.: Consideration on a Color Reflection Model for Object Surfaces. IPSJ, 1988-CVIM-059 (1989) (in Japanese)Google Scholar
  8. 8.
    Saito, T.: Method and Apparatus for Illumination Chromaticity Measurement of a Color Image. Japanese Patent 2508237 (1989) (in Japanese)Google Scholar
  9. 9.
    Lehmann, T.M., Palm, C.: Color Line Search for Illuminant Estimation in Real-World Scenes. J. Opt. Soc. Am. A 18(11), 2679–2691 (2001)CrossRefGoogle Scholar
  10. 10.
    Finlayson, G.D., Schaefer, G.: Solving for Colour Constancy using a Constrained Dichromatic Reflection Model. IJCV 42(3), 127–144 (2001)CrossRefzbMATHGoogle Scholar
  11. 11.
    Ebner, M., Herrmann, C.: On Determining the Color of the Illuminant Using the Dichromatic Reflection Model. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 1–8. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Tajima, J., Ikeda, T.: High Quality Color Image Quantization, Utilizing Human Vision Characteristics. J. IIEEJ, 293–301 (1989) (in Japanese)Google Scholar
  13. 13.
    Ohtsu, N.: A Threshold Selection Method from Gray-Level Histograms. IEEE Trans. SMC-9(1), 62–66 (1979)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Johji Tajima
    • 1
  1. 1.Nagoya City UniversityNagoyaJapan

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