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Joint Pattern Recognition Symposium

DAGM 2003: Pattern Recognition pp 60–67Cite as

Combining White-Patch Retinex and the Gray World Assumption to Achieve Color Constancy for Multiple Illuminants

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2781))

Abstract

The human visual system is able to correctly determine the color of objects irrespective of the actual light they reflect. This ability to compute color constant descriptors is an important problem for computer vision research. We have developed a parallel algorithm for color constancy. The algorithm is based on two fundamental theories of color constancy, the gray world assumption and the white-patch retinex algorithm. The algorithm’s performance is demonstrated on several images where objects are illuminated by multiple illuminants.

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References

  1. Barnard, K., Finlayson, G., Funt, B.: Color constancy for scenes with varying illumination. Computer Vision and Image Understanding 65(2), 311–321 (1997)

    Article  Google Scholar 

  2. Brainard, D.H., Wandell, B.A.: Analysis of the retinex theory of color vision. In: Healey, G.E., Shafer, S.A., Wolff, L.B. (eds.) Color, Boston, pp. 208–218. Jones and Bartlett Publishers, USA (1992)

    Google Scholar 

  3. Brill, M., West, G.: Contributions to the theory of invariance of color under the condition of varying illumination. Journal of Math. Biology 11, 337–350 (1981)

    Article  MATH  MathSciNet  Google Scholar 

  4. Buchsbaum, G.: A spatial processor model for object colour perception. Journal of the Franklin Institute 310(1), 337–350 (1980)

    Article  Google Scholar 

  5. Cardei, V.C., Funt, B.: Committee-based color constancy. In: Proc. of the IS&T/SID 7th Color Imaging Conference: Color Science, Systems and Applications, pp. 311–313 (1999)

    Google Scholar 

  6. Courtney, S.M., Finkel, L.H., Buchsbaum, G.: A multistage neural network for color constancy and color induction. IEEE Trans. on Neural Networks 6(4), 972–985 (1995)

    Article  Google Scholar 

  7. D’Zmura, M., Lennie, P.: Mechanisms of color constancy. In: Healey, G.E., Shafer, S.A., Wolff, L.B. (eds.) Color, Boston, pp. 224–234. Jones and Bartlett Publishers, USA (1992)

    Google Scholar 

  8. Ebner, M.: A parallel algorithm for color constancy. Technical Report 296, Universität Würzburg, Lehrstuhl für Informatik II, Würzburg, Germany (April 2002)

    Google Scholar 

  9. Ebner, M.: Evolving color constancy for an artificial retina. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tetamanzi, A.G.B., Langdon, W.B. (eds.) EuroGP 2001. LNCS, vol. 2038, pp. 11–22. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  10. Finlayson, G.D.: Color in perspective. IEEE Trans. on Pattern Analysis and Machine Intelligence 18(10), 1034–1038 (1996)

    Article  Google Scholar 

  11. Finlayson, G.D., Hubel, P.M., Hordley, S.: Color by correlation. In: Proc. of IS&T/SID. The 5th Color Imaging Conference: Color Science, Systems, and Applications, The Radisson Resort, Scottsdale, AZ, pp. 6–11 (1997)

    Google Scholar 

  12. Finlayson, G.D., Schiele, B., Crowley, J.L.: Comprehensive colour image normalization. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, p. 475. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  13. Forsyth, D.A.: A novel approach to colour constancy. In: 2nd Int. Conf. on Computer Vision, Tampa, FL, pp. 9–18. IEEE Press, Los Alamitos (1988)

    Google Scholar 

  14. Funt, B., Barnard, K., Martin, L.: Is colour constancy good enough? In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 445–459. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  15. Funt, B., Cardei, V., Barnard, K.: Learning color constancy. In: Proc. of the IS&T/SID 4th Color Imaging Conference, Scottsdale, pp. 58–60 (1996)

    Google Scholar 

  16. Funt, B.V., Drew, M.S.: Color constancy computation in near-mondrian scenes using a finite dimensional linear model. In: Proc. of the Comp. Society Conf. on Computer Vision and Pattern Recognition, pp. 544–549. Comp. Society Press (1988)

    Google Scholar 

  17. Gershon, R., Jepson, A.D., Tsotsos, J.K.: From [r,g,b] to surface reflectance: Computing color constant descriptors in images. In: Proc. of the 10th Int. Joint Conference on Artificial Intelligence, vol. 2, pp. 755–758 (1987)

    Google Scholar 

  18. Horn, B.K.P.: Robot Vision. The MIT Press, Cambridge (1986)

    Google Scholar 

  19. Land, E.H.: The retinex theory of colour vision. Proc. Royal Inst. Great Britain 47, 23–58 (1974)

    Google Scholar 

  20. Linnell, K.J., Foster, D.H.: Space-average scene colour used to extract illuminant information. In: Dickinson, C., Murray, I., Carden, D. (eds.) John Dalton’s Colour Vision Legacy. Selected Proc. of the Int. Conf., London, pp. 501–509. Taylor & Francis, Abington (1997)

    Google Scholar 

  21. Maloney, L.T., Wandell, B.A.: Color constancy: a method for recovering surface spectral reflectance. Journal of the Opt. Society of America A3 3(1), 29–33 (1986)

    Article  Google Scholar 

  22. Moore, A., Allman, J., Goodman, R.M.: A real-time neural system for color constancy. IEEE Trans. on Neural Networks 2(2), 237–247 (1991)

    Article  Google Scholar 

  23. Usui, S., Nakauchi, S.: A neurocomputational model for colour constancy. In: Dickinson, C., Murray, I., Carden, D. (eds.) John Dalton’s Colour Vision Legacy. Selected Proc. of the Int. Conf., London, pp. 475–482. Taylor & Francis, Abington (1997)

    Google Scholar 

  24. Zeki, S.: A Vision of the Brain. Blackwell Science, Oxford (1993)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Lehrstuhl für Informatik II, Universität Würzburg, Am Hubland, 97074, Würzburg, Germany

    Marc Ebner

Authors
  1. Marc Ebner
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Editor information

Editors and Affiliations

  1. Otto-von-Guericke University, 39106, Magdeburg, Germany

    Bernd Michaelis

  2. Institute for Electronics, Signal Processing and Communications (IESK), Otto-von-Guericke-University Magdeburg, P.O. Box 4120, D-39016, Magdeburg, Germany

    Gerald Krell

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Ebner, M. (2003). Combining White-Patch Retinex and the Gray World Assumption to Achieve Color Constancy for Multiple Illuminants. In: Michaelis, B., Krell, G. (eds) Pattern Recognition. DAGM 2003. Lecture Notes in Computer Science, vol 2781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45243-0_9

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  • DOI: https://doi.org/10.1007/978-3-540-45243-0_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40861-1

  • Online ISBN: 978-3-540-45243-0

  • eBook Packages: Springer Book Archive

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