Advertisement

Color Constancy Using Local Color Shifts

  • Marc Ebner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3023)

Abstract

The human visual system is able to correctly determine the color of objects in view irrespective of the illuminant. This ability to compute color constant descriptors is known as color constancy. We have developed a parallel algorithm for color constancy. This algorithm is based on the computation of local space average color using a grid of processing elements. We have one processing element per image pixel. Each processing element has access to the data stored in neighboring elements. Local space average color is used to shift the color of the input pixel in the direction of the gray vector. The computations are executed inside the unit color cube. The color of the input pixel as well as local space average color is simply a vector inside this Euclidean space. We compute the component of local space average color which is orthogonal to the gray vector. This component is subtracted from the color of the input pixel to compute a color corrected image. Before performing the color correction step we can also normalize both colors. In this case, the resulting color is rescaled to the original intensity of the input color such that the image brightness remains unchanged.

Keywords

Processing Element Human Visual System Color Constancy Local Color Current Pixel 
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.
    Land, E.H.: The retinex theory of colour vision. Proc. Royal Inst. Great Britain 47, 23–58 (1974)Google Scholar
  2. 2.
    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
  3. 3.
    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.), pp. 208–218. Jones and Bartlett Publishers, Color(1992)Google Scholar
  4. 4.
    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)zbMATHMathSciNetCrossRefGoogle Scholar
  5. 5.
    Funt, B.V., Drew, M.S.: Color constancy computation in near-mondrian scenes using a finite dimensional linear model. In: Jain, R., Davis, L. (eds.) Proceedings of the Computer Society Conference on Computer Vision and Pattern Recognition, pp. 544–549. Computer Society Press, Ann Arbor, MI (1988)CrossRefGoogle Scholar
  6. 6.
    Horn, B.K.P.: Determining lightness from an image. Computer Graphics and Image Processing 3, 277–299 (1974)CrossRefGoogle Scholar
  7. 7.
    Horn, B.K.P.: Robot Vision. The MIT Press, Cambridge (1986)Google Scholar
  8. 8.
    Rahman, Z., Jobson, D.J., Woodell, G.A.: Method of improving a digital image. United States Patent No. 5,991,456 (1999)Google Scholar
  9. 9.
    Barnard, K., Finlayson, G., Funt, B.: Color constancy for scenes with varying illumination. Computer Vision and Image Understanding 65, 311–321 (1997)CrossRefGoogle Scholar
  10. 10.
    Forsyth, D.A.: A novel approach to colour constancy. In: Second International Conference on Computer Vision, December 5-8, pp. 9–18. IEEE Press, Los Alamitos (1988)Google Scholar
  11. 11.
    Forsyth, D.A.: A novel algorithm for color constancy. In: Healey, G.E., Shafer, S.A., Wolff, L.B. (eds.), pp. 241–271. Jones and Bartlett Publishers, Color, Boston (1992)Google Scholar
  12. 12.
    Finlayson, G.D.: Color in perspective. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 1034–1038 (1996)CrossRefGoogle Scholar
  13. 13.
    Barnard, K., Martin, L., Funt, B.: Colour by correlation in a three dimensional colour space. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 375–389. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  14. 14.
    Finlayson, G.D., Hubel, P.M., Hordley, S.: Color by correlation. In: Proceedings of IS&T/SID. The Fifth Color Imaging Conference: Color Science, Systems, and Applications, The Radisson Resort, Scottsdale, AZ, November 17-20, pp. 6–11 (1997)Google Scholar
  15. 15.
    Buchsbaum, G.: A spatial processor model for object colour perception. Journal of the Franklin Institute 310, 337–350 (1980)CrossRefGoogle Scholar
  16. 16.
    Gershon, R., Jepson, A.D., Tsotsos, J.K.: From [R,G,B] to surface reflectance: Computing color constant descriptors in images. In: McDermott, J.P. (ed.) Proc. of the 10th Int. Joint Conf. on Artificial Intelligence, Milan, Italy, vol. 2, pp. 755–758. Morgan Kaufmann, San Francisco (1987)Google Scholar
  17. 17.
    Funt, B.V., Drew, M.S., Ho, J.: Color constancy from mutual reflection. International Journal of Computer Vision 6, 5–24 (1991)CrossRefGoogle Scholar
  18. 18.
    Ho, J., Funt, B.V., Drew, M.S.: Separating a color signal into illumination and surface reflectance components: Theory and applications. In: Healey, G.E., Shafer, S.A., Wolff, L.B. (eds.), pp. 272–283. Jones and Bartlett Publishers, Color, Boston (1992)Google Scholar
  19. 19.
    Maloney, L.T., Wandell, B.A.: Color constancy: a method for recovering surface spectral reflectance. Journal of the Optical Society of America A3 3, 29–33 (1986)CrossRefGoogle Scholar
  20. 20.
    D’Zmura, M., Lennie, P.: Mechanisms of color constancy. In: Healey, G.E., Shafer, S.A., Wolff, L.B. (eds.), pp. 224–234. Jones and Bartlett Publishers, Color, Boston (1992)Google Scholar
  21. 21.
    Courtney, S.M., Finkel, L.H., Buchsbaum, G.: A multistage neural network for color constancy and color induction. IEEE Trans. on Neural Networks 6, 972–985 (1995)CrossRefGoogle Scholar
  22. 22.
    Dufort, P.A., Lumsden, C.J.: Color categorization and color constancy in a neural network model of v4. Biological Cybernetics 65, 293–303 (1991)CrossRefGoogle Scholar
  23. 23.
    Funt, B., Cardei, V., Barnard, K.: Learning color constancy. In: Proceedings of the IS &T/SID Fourth Color Imaging Conference, Scottsdale, pp. 58–60 (1996)Google Scholar
  24. 24.
    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
  25. 25.
    Moore, A., Allman, J., Goodman, R.M.: A real-time neural system for color constancy. IEEE Transactions on Neural Networks 2, 237–247 (1991)CrossRefGoogle Scholar
  26. 26.
    Novak, C.L., Shafer, S.A.: Supervised color constancy for machine vision. In: Healey, G.E., Shafer, S.A., Wolff, L.B. (eds.), pp. 284–299. Jones and Bartlett Publishers, Color, Boston (1992)Google Scholar
  27. 27.
    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, London (1997)Google Scholar
  28. 28.
    Finlayson, G.D., Schiele, B., Crowley, J.L.: Comprehensive colour image normalization. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 475–490. Springer, Heidelberg (1998)Google Scholar
  29. 29.
    Cardei, V.C., Funt, B.: Committee-based color constancy. In: Proceedings of the IS&T/SID Seventh Color Imaging Conference: Color Science, Systems and Applications, Scottsdale, Arizona, pp. 311–313 (1999)Google Scholar
  30. 30.
    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)CrossRefGoogle Scholar
  31. 31.
    Risson, V.J.: Determination of an illuminant of digital color image by segmentation and filtering. United States Patent Application, Pub. No. US 2003/0095704 A1 (2003)Google Scholar
  32. 32.
    Ebner, M.: A parallel algorithm for color constancy. Technical Report 296, Universit ät Würzburg, Lehrstuhl für Informatik II, Am Hubland, 97074 Würzburg, Germany (2002)Google Scholar
  33. 33.
    Ebner, M.: Combining white-patch retinex and the gray world assumption to achieve color constancy for multiple illuminants. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 60–67. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  34. 34.
    Ebner, M.: A parallel algorithm for color constancy. Journal of Parallel and Distributed Computing 64, 79–88 (2004)CrossRefGoogle Scholar
  35. 35.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley Publishing Company, Reading (1992)Google Scholar
  36. 36.
    Hanbury, A., Serra, J.: Colour image analysis in 3d-polar coordinates. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 124–131. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  37. 37.
    Koosh, V.F.: Analog Computation and Learning in VLSI. PhD thesis, California Institute of Technology Pasadena, California (2001)Google Scholar
  38. 38.
    Jain, R., Kasturi, R., Schunck, B.G.: Machine Vision. McGraw-Hill, Inc., New York (1995)Google Scholar
  39. 39.
    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, 439–476 (1938)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Marc Ebner
    • 1
  1. 1.Lehrstuhl für Informatik IIUniversität WürzburgAm HublandGermany

Personalised recommendations