Region-Based Illuminant Estimation for Effective Color Correction

  • Simone Bianco
  • Francesca Gasparini
  • Raimondo Schettini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)

Abstract

Several algorithms were proposed in the literature to recover the illuminant chromaticity of the original scene. These algorithms work well only when prior assumptions are satisfied, and the best and the worst algorithms may be different for different scenes. In particular for certain images a do nothing strategy can be preferred. Starting from these considerations, we have developed a region-based color constancy algorithm able to automatically select (and/or blend) among different color corrections, including a conservative do nothing strategy. The strategy to be applied is selected without any a priori knowledge of the image content and only performing image low level analysis.

Keywords

Particle Swarm Optimization Color Histogram Color Correction Equivalent Circle Pattern Search Method 
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.

References

  1. 1.
    Hordley, S.D.: Scene illuminant estimation: Past, present, and future. Color Res. Appl. 31(4), 303–314 (2006)CrossRefGoogle Scholar
  2. 2.
    Bianco, S., Gasparini, F., Schettini, R.: A consensus based framework for illuminant chromaticity estimation. Journal of Electronic Imaging 17, 023013-1–023013-9 (2008)Google Scholar
  3. 3.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)Google Scholar
  4. 4.
    Ciurea, F., Funt, B.: A Large Image Database for Color Constancy Research. In: Proc. IS&T/SID 11th Color Imaging Conference, pp. 160–164 (2003)Google Scholar
  5. 5.
    Ciocca, G., Schettini, R.: An Innovative Algorithm for Key Frame Extraction in Video Summarization. Journal of Real-Time Image Processing 1(1), 69–88 (2006)CrossRefGoogle Scholar
  6. 6.
    Ciocca, G., Schettini, R.: Supervised And Unsupervised Classification Post-Processing for Visual Video Summaries. IEEE Transactions on Consumer Electronics 2(52), 630–638 (2006)CrossRefGoogle Scholar
  7. 7.
    Bianco, S., Ciocca, G., Cusano, C., Schettini, R.: Classification-based Color Constancy. In: Sebillo, M., Vitiello, G., Schaefer, G. (eds.) VISUAL 2008. LNCS, vol. 5188, pp. 104–113. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Bianco, S., Ciocca, G., Cusano, C., Schettini, R.: Improving Color Constancy Using Indoor–Outdoor Image Classification. IEEE Transactions on Image Processing 17(12), 2381–2392 (2008)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Hordley, S.D., Finlayson, G.D.: Re-evaluating Color Constancy Algorithms. In: Proc. 17th International Conference on Pattern Recognition, pp. 76–79 (2004)Google Scholar
  10. 10.
    Land, E.: The retinex theory of color vision. Scientific American 237(6), 108–128 (1977)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Ebner, F., Fairchild, M.D.: IDevelopment and Testing of a Color Space (IPT) with Improved Hue Uniformity. In: IS&T/SID Sixth Color Imaging Conference: Color Science, Systems and Applications, vol. 6, pp. 8–13 (1998)Google Scholar
  12. 12.
    Lewis, R.M., Torczon, V.: Pattern search algorithms for bound constrained minimization. SIAM Journal on Optimization 9, 1082–1099 (1999)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Lewis, R.M., Torczon, V.: Pattern search methods for linearly constrained minimization. SIAM Journal on Optimization 10, 917–941 (2000)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Buchsbaum, G.: A spatial processor model for object color perception. Journal of Franklin Institute 310, 1–26 (1980)CrossRefGoogle Scholar
  15. 15.
    Cardei, V., Funt, B., Barndard, K.: White point estimation for uncalibrated images. In: Proc. IS&T/SID 7th Color Imaging Conference, pp. 97–100 (1999)Google Scholar
  16. 16.
    Finlayson, G., Trezzi, E.: Shades of gray and colour constancy. In: Proc. IS&T/SID 12th Color Imaging Conference, pp. 37–41 (2004)Google Scholar
  17. 17.
    Barnard, K., Cardei, V., Funt, B.: A comparison of computational color constancy algorithms; part two: Experiments with image data. IEEE Tansactions on Image Processing 11(9), 985–996 (2002)CrossRefGoogle Scholar
  18. 18.
    van de Weijer, J., Gevers, T., Gijsenij, A.: Edge-based Color Constancy. IEEE Transactions on Image Processing 16(9), 2207–2214 (2007)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Funt, B., Barnard, K., Martin, L.: Is machine colour constancy good enough? In: Proc. 5th European Conference on Computer Vision, pp. 445–459 (1998)Google Scholar
  20. 20.
    Fairchild, M.D.: Color Appearance Models. Addison Wesley, Reading (1997)Google Scholar
  21. 21.
    Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics 1, 80–83 (1945)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Simone Bianco
    • 1
  • Francesca Gasparini
    • 1
  • Raimondo Schettini
    • 1
  1. 1.DISCo (Dipartimento di Informatica, Sistemistica e Comunicazione)Università degli Studi di Milano-BicoccaMilanoItaly

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