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Specularity, the Zeta-image, and Information-Theoretic Illuminant Estimation

  • Mark S. Drew
  • Hamid Reza Vaezi Joze
  • Graham D. Finlayson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7584)

Abstract

Identification of illumination is an important problem in imaging. In this paper we present a new and effective physics-based colour constancy algorithm which makes use of a novel log-relative-chromaticity planar constraint. We call the new feature the Zeta-image. We show that this new feature is tied to a novel application of the Kullback-Leibler Divergence, here applied to chromaticity values instead of probabilities. The new method requires no training data or tunable parameters. Moreover it is simple to implement and very fast. Our experimental results across datasets of real images show the proposed method significantly outperforms other unsupervised methods while its estimation accuracy is comparable with more complex, supervised, methods. As well, the new planar constraint can be used as a post-processing stage for any candidate colour constancy method in order to improve its accuracy.

Keywords

Angular Error Colour Constancy Planar Constraint Specular Component Specular Point 
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 Research and Application 31, 303–314 (2006)CrossRefGoogle Scholar
  2. 2.
    Gijsenij, A., Gevers, T., van de Weijer, J.: Computational color constancy: Survey and experiments. IEEE Trans. on Image Proc. 20 (2011)Google Scholar
  3. 3.
    van de Weijer, J., Gevers, T.: Color constancy based on the grey-edge hypothesis. In: Int. Conf. on Image Proc., pp. II:722–II:725 (2005)Google Scholar
  4. 4.
    Forsyth, D.: A novel approach to color constancy. In: Int. Conf. on Computer Vision, pp. 9–18 (1988)Google Scholar
  5. 5.
    Lee, H.: Method for computing the scene-illuminant chromaticity from specular highlights. J. Opt. Soc. Am. A 3, 1694–1699 (1986)CrossRefGoogle Scholar
  6. 6.
    Lehmann, T.M., Palm, C.: Color line search for illuminant estimation in real-world scenes. J. Opt. Soc. Amer. A 18, 2679–2691 (2001)CrossRefGoogle Scholar
  7. 7.
    Tan, R., Nishino, K., Ikeuchi, K.: Color constancy through inverse-intensity chromaticity space. J. Opt. Soc. Am. A 21, 321–334 (2004)CrossRefGoogle Scholar
  8. 8.
    Borges, C.: Trichromatic approximation method for surface illumination. J. Opt. Soc. Am. A 8, 1319–1323 (1991)CrossRefGoogle Scholar
  9. 9.
    Drew, M., Finlayson, G.: Multispectral processing without spectra. J. Opt. Soc. Am. A 20, 1181–1193 (2003)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Choudhury, A., Medioni, G.: Perceptually motivated automatic color contrast enhancement. In: Color and Reflec. in Imaging and Comp. Vis. Workshop, pp. 1893–1900 (2009)Google Scholar
  11. 11.
    Funt, B.V., Shi, L.: The rehabilitation of MaxRGB. In: 18th Color Imaging Conf., pp. 256–259 (2010)Google Scholar
  12. 12.
    Barnard, K., Martin, L., Funt, B.V., Coath, A.: A data set for colour research. Color Res. and Applic. 27, 147–151 (2002)CrossRefGoogle Scholar
  13. 13.
    Shi, L., Funt, B.V.: Re-processed version of the Gehler color constancy dataset of 568 images (2010), http://www.cs.sfu.ca/~colour/data/
  14. 14.
    Ciurea, F., Funt, B.V.: A large image database for color constancy research. In: Color Imag. Conf., pp. 160–164 (2003)Google Scholar
  15. 15.
    Gehler, P., Rother, C., Blake, A., Minka, T., Sharp, T.: Bayesian color constancy revisited. In: Comp. Vis. and Patt. Rec. (2008)Google Scholar
  16. 16.
    Gijsenij, A.: Color constancy: Research website on illuminant estimation (2012), http://staff.science.uva.nl/~gijsenij/colorconstancy/
  17. 17.
    Gijsenij, A., Gevers, T., Weijer, J.: Generalized gamut mapping using image derivative structures for color constancy. Int. J. of Comp. Vis. 86, 127–139 (2008)CrossRefGoogle Scholar
  18. 18.
    Gijsenij, A., Gevers, T.: Color constancy using natural image statistics and scene semantics. Trans. on Patt. Anal. and Mach. Intell. 33, 687–698 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mark S. Drew
    • 1
  • Hamid Reza Vaezi Joze
    • 1
  • Graham D. Finlayson
    • 2
  1. 1.School of Computing ScienceSimon Fraser UniversityCanada
  2. 2.School of Computing SciencesUniversity of East AngliaUK

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