A Colour Constancy Algorithm Based on the Histogram of Feasible Colour Mappings

  • Jaume Vergés-Llahí
  • Alberto Sanfeliu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2905)


Colour is an important cue in many applications in machine vision and image processing. Nevertheless, colour greatly depends upon illumination changes. Colour constancy goal is to keep colour images stable. This paper’s contribution to colour constancy lies in estimating both the set and the likelihood of feasible colour mappings. Then, the most likely mapping is selected and the image is rendered as it would be seen under a canonical illuminant. This approach is helpful in tasks where light can be neither controlled nor easily measured since it only makes use of image data, avoiding a common drawback in other colour constancy algorithms. Finally, we check its performance using several sets of images of objects under quite different illuminants and the results are compared to those obtained if the true illuminant colour were known.


Colour colour mappings colour change colour constancy colour histograms 


  1. 1.
    Finlayson, G., Hordley, S., Hubel, P.: Colour by correlation: A simple, unifying framework for colour constancy. IEEE Trans. on Pattern Analysis and Machine Intelligence 23, 1209–1221 (2001)CrossRefGoogle Scholar
  2. 2.
    Forsyth, D.: A novel algorithm for color constancy. Int. Journal of Computer Vision 5, 5–36 (1990)CrossRefGoogle Scholar
  3. 3.
    Finlayson, G.: Color in perspective. IEEE Trans. on Pattern Analysis and Machine Intelligence 18, 1034–1038 (1996)CrossRefGoogle Scholar
  4. 4.
    Barnard, K., Cardei, V., Funt, B.: A comparison of computational colour constancy algorithms: Part one: Methodology and experiments with synthesized data. IEEE Trans. on Image Processing 11, 972–983 (2002)CrossRefGoogle Scholar
  5. 5.
    Barnard, K., Martin, L., Coath, A., Funt, B.: A comparison of computational colour constancy algorithms: Part two: Experiments with image data. IEEE Trans. on Image Processing 11, 985–996 (2002)CrossRefGoogle Scholar
  6. 6.
    Sapiro, G.: Color and illuminant voting. IEEE Trans. on Pattern Analysis and Machine Intelligence 21, 1210–1215 (1999)CrossRefGoogle Scholar
  7. 7.
    Funt, B., Barnard, K., Martin, L.: Is colour constancy good enough? In: Proc. 5th European Conference Computer Vision, pp. 445–459 (1998)Google Scholar
  8. 8.
    Finlayson, G., Hordley, S.: Improving gamut mapping color constancy. IEEE Trans. on Image Processing 9, 1774–1783 (2000)CrossRefGoogle Scholar
  9. 9.
    Swain, M., Ballard, D.: Indexing via color histograms. In: Proc. Int. Conf. on Computer Vision, pp. 390–393 (1990)Google Scholar
  10. 10.
    Schiele, B., Crowley, J.: Object recognition using multidimensional receptive field histograms. In: Proc. Int. European Conf. on Computer Vision, pp. 610–619 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jaume Vergés-Llahí
    • 1
  • Alberto Sanfeliu
    • 1
  1. 1.Institut de Robòtica i Informàtica IndustrialTechnological Park of BarcelonaBarcelona

Personalised recommendations