Advertisement

How Does the Brain Arrive at a Color Constant Descriptor?

  • Marc Ebner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4729)

Abstract

Color is not a physical quantity which can be measured. Yet we attach it to the objects around us. Colors appear to be approximately constant to a human observer. They are an important cue in everyday life. Today, it is known that the corpus callosum plays an important role in color perception. Area V4 contains cells which seem to respond to the reflectance of an object irrespective of the wavelength composition of the light reflected by the object. What is not known is how the brain arrives at a color constant or approximately color constant descriptor. A number of theories about color perception have been put forward. Most theories are phenomenological descriptions of color vision. However, what is needed in order to understand how the visual system works is a computational theory. With this contribution we describe a computational theory for color perception which is much simpler in comparison to previously published theories yet effective at computing a color constant descriptor.

Keywords

Corpus Callosum Processing Element Color Vision Computational Theory Color Constancy 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hedgecoe, J.: Fotografieren: die neue große Fotoschule. Dorling Kindersley Verlag GmbH, Starnberg (2004)Google Scholar
  2. 2.
    Jacobsen, R.E., Ray, S.F., Attridge, G.G., Axford, N.R.: The Manual of Photography. Photographic and Digital Imaging. Focal Press, Oxford (2000)Google Scholar
  3. 3.
    Zeki, S.: A Vision of the Brain. Blackwell Science, Oxford (1993)Google Scholar
  4. 4.
    Land, E.H.: The retinex. American Scientist 52, 247–264 (1964)Google Scholar
  5. 5.
    Land, E.H.: The retinex theory of colour vision. Proc. Royal Inst. Great Britain 47, 23–58 (1974)Google Scholar
  6. 6.
    Marr, D.: Vision. W. H. Freeman and Company, New York (1982)Google Scholar
  7. 7.
    Land, E.H., McCann, J.J.: Lightness and retinex theory. Journal of the Optical Society of America 61(1), 1–11 (1974)Google Scholar
  8. 8.
    Horn, B.K.P.: Determining lightness from an image. Computer Graphics and Image Processing 3, 277–299 (1974)Google Scholar
  9. 9.
    Land, E.H.: An alternative technique for the computation of the designator in the retinex theory of color vision. Proc. Natl. Acad. Sci. USA 83, 3078–3080 (1986)CrossRefGoogle Scholar
  10. 10.
    Hurlbert, A.: Formal connections between lightness algorithms. J. Opt. Soc. Am. A 3(10), 1684–1693 (1986)Google Scholar
  11. 11.
    Blake, A.: Boundary conditions for lightness computation in mondrian world. Computer Vision, Graphics, and Image Processing 32, 314–327 (1985)CrossRefGoogle Scholar
  12. 12.
    Rahman, Z., Jobson, D.J., Woodell, G.A.: Method of improving a digital image. United States Patent No. 5,991,456 (1999)Google Scholar
  13. 13.
    Buchsbaum, G.: A spatial processor model for object colour perception. Journal of the Franklin Institute 310(1), 337–350 (1980)CrossRefGoogle Scholar
  14. 14.
    Forsyth, D.A.: A novel approach to colour constancy. In: Second International Conference on Computer Vision, Tampa, FL, December 5-8, 1988, pp. 9–18. IEEE Press, Los Alamitos (1988)CrossRefGoogle Scholar
  15. 15.
    Finlayson, G.D.: Color in perspective. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(10), 1034–1038 (1996)CrossRefGoogle Scholar
  16. 16.
    Barnard, K., Finlayson, G., Funt, B.: Color constancy for scenes with varying illumination. Computer Vision and Image Understanding 65(2), 311–321 (1997)CrossRefGoogle Scholar
  17. 17.
    Paulus, D., Csink, L., Niemann, H.: Color cluster rotation. In: ICIP. Proc. of the Int. Conf. on Image Processing, pp. 161–165. IEEE Computer Society Press, Los Alamitos (1998)Google Scholar
  18. 18.
    Finlayson, G.D., Schiele, B., Crowley, J.L.: Comprehensive colour image normalization. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 475–490. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  19. 19.
    Ebner, M.: Color constancy using local color shifts. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3023, pp. 276–287. Springer, Heidelberg (2004)Google Scholar
  20. 20.
    Ebner, M.: A parallel algorithm for color constancy. Journal of Parallel and Distributed Computing 64(1), 79–88 (2004)CrossRefGoogle Scholar
  21. 21.
    Ebner, M.: Evolving color constancy. Special Issue on Evolutionary Computer Vision and Image Understanding of Pattern Recognition Letters 27(11), 1220–1229 (2006)Google Scholar
  22. 22.
    Helson, H.: Fundamental problems in color vision. I. the principle governing changes in hue, saturation, and lightness of non-selective samples in chromatic illumination. Journal of Experimental Psychology 23(5), 439–476 (1938)CrossRefGoogle Scholar
  23. 23.
    Horn, B.K.P.: Robot Vision. MIT Press, Cambridge, Massachusetts (1986)Google Scholar
  24. 24.
    Moore, A., Allman, J., Goodman, R.M.: A real-time neural system for color constancy. IEEE Transactions on Neural Networks 2(2), 237–247 (1991)CrossRefGoogle Scholar
  25. 25.
    Ebner, M.: Color Constancy. John Wiley & Sons, England (2007)Google Scholar
  26. 26.
    Judd, D.B.: Hue saturation and lightness of surface colors with chromatic illumination. Journal of the Optical Society of America 30, 2–32 (1940)Google Scholar
  27. 27.
    Richards, W., Parks, E.A.: Model for color conversion. Journal of the Optical Society of America 61(7), 971–976 (1971)Google Scholar
  28. 28.
    Zeki, S., Marini, L.: Three cortical stages of colour processing in the human brain. Brain 121, 1669–1685 (1998)CrossRefGoogle Scholar
  29. 29.
    Zeki, S., Bartels, A.: The clinical and functional measurement of cortical (in)activity in the visual brain, with special reference to the two subdivisions (V4 and V4α) of the human colour centre. Proc. R. Soc. Lond. B 354, 1371–1382 (1999)Google Scholar
  30. 30.
    Land, E.H., Hubel, D.H., Livingstone, M.S., Perry, S.H., Burns, M.M.: Colour-generating interactions across the corpus callosum. Nature 303, 616–618 (1983)CrossRefGoogle Scholar
  31. 31.
    D’Zmura, M., Lennie, P.: Mechanisms of color constancy. In: Healey, G.E., Shafer, S.A., Wolff, L.B. (eds.) Color, pp. 224–234. Jones and Bartlett Publishers, Boston (1992)Google Scholar
  32. 32.
    Herault, J.: A model of colour processing in the retina of vertebrates: From photoreceptors to colour opposition and colour constancy phenomena. Neurocomputing 12, 113–129 (1996)zbMATHCrossRefGoogle Scholar
  33. 33.
    Livingstone, M.S., Hubel, D.H.: Anatomy and physiology of a color system in the primate visual cortex. The Journal of Neuroscience 4(1), 309–356 (1984)Google Scholar
  34. 34.
    Faugeras, O.D.: Digital color image processing within the framework of a human visual model. IEEE Transactions on Acoustics, Speech, and Signal Processing ASSP-27(4), 380–393 (1979)CrossRefGoogle Scholar
  35. 35.
    Hunt, R.W.G.: Light energy and brightness sensation. Nature 179, 1026–1027 (1957)CrossRefGoogle Scholar
  36. 36.
    Glasser, L.G., McKinney, A.H., Reilly, C.D., Schnelle, P.D.: Cube-root color coordinate system. Journal of the Optical Society of America 48(10), 736–740 (1958)CrossRefGoogle Scholar
  37. 37.
    International Commission on Illumination: Colorimetry, 2nd edn., corrected reprint. Technical Report 15.2, International Commission on Illumination (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Marc Ebner
    • 1
  1. 1.Universität Würzburg, Lehrstuhl für Informatik II, Am Hubland, 97074 WürzburgGermany

Personalised recommendations