Abstract
Statistics-based colour constancy algorithms work well as long as there are many colours in a scene, they fail however when the encountering scenes comprise few surfaces. In contrast, physics-based algorithms, based on an understanding of physical processes such as highlights and interreflections, are theoretically able to solve for colour constancy even when there are as few as two surfaces in a scene. Unfortunately, physics-based theories rarely work outside the lab. In this paper we show that a combination of physical and statistical knowledge leads to a surprisingly simple and powerful colour constancy algorithm, one that also works well for images of natural scenes.
From a physical standpoint we observe that given the dichromatic model of image formation the colour signals coming from a single uniformly-coloured surface are mapped to a line in chromaticity space. One component of the line is defined by the colour of the illuminant (i.e. specular highlights) and the other is due to its matte, or Lambertian, reflectance. We then make the statistical observation that the chromaticities of common light sources all follow closely the Planckian locus of black-body radiators. It follows that by intersecting the dichromatic line with the Planckian locus we can estimate the chromaticity of the illumination. We can solve for colour constancy even when there is a single surface in the scene. When there are many surfaces in a scene the individual estimates from each surface are averaged together to improve accuracy.
In a set of experiments on real images we show our approach delivers very good colour constancy. Moreover, performance is significantly better than previous dichromatic algorithms.
Similar content being viewed by others
References
Barnard, K., Finlayson, G.D., and Funt, B.V. 1997. Color constancy for scenes with varying illumination. Computer Vision and Image Understanding, 65(2):311-321.
CIE. 1986. Colorimetry. CIE Publications 15.2, Commission International de L'Eclairage, 2nd ed.
D'Zmura, M. and Lennie, P. 1986. Mechanisms of color constancy. Journal of the Optical Society of America A, 3(10):1662-1672.
Finlayson, G.D. 1996. Color in perspective. IEEE Trans. PAMI, 18(10):1034-1038.
Finlayson, G.D. and Funt, B.V. 1994. Colour constancy using shadows. Perception, 23:89-90.
Finlayson, G.D., Hubel, P.M., and Hordley, S. 1997. Color by correlation. In The 5th Color Imaging Conference, Scottsdale, Arizona, pp. 6-11.
Finlayson, G.D., Hubel, P.M., and Hordley, S. 1999. Colour by correlation: A simple, unifying approach to colour constancy. In Proceedings of the 7th International Conference on Computer Vision, Kerkyra, Greece, pp. 835-842.
Forsyth, D.A. 1990. A novel algorithm for colour constancy. Int. Journal Computer Vision, 5(1):5-36.
Funt, B.V., Drew, M.S., and Ho, J. 1991. Color constancy from mutual reflection. Int. Journal Computer Vision, 6(1):5-24.
Funt, B.V. and Ho, J. 1989. Color from black and white. Int. Journal Computer Vision, 3(2):109-118.
Healey, G. 1989. Using color for geometry-insensitive segmentation. Journal of the Optical Society of America A, 6(6):920-937.
IEC. 1998. Default RGB colour space-sRGB. Standards document.
Klinker, G.J., Shafer, S.A., and Kanade, T. 1998. The measurement of high-lights in color images. Int. Journal Computer Vision, 2(1):7-32.
Lee, H.-C. 1986. Method for computing the scene-illuminant from specular highlights. Journal of the Optical Society of America A, 3(10):1694-1699.
Lee, H.-C. 1990. Illuminant color from shading. In Proceedings of Perceiving, Measuring and Using Color, SPIE Vol. 1250, pp. 236-244.
McCamy, C.S., Marcus, H., and Davidson, J.G. 1976. A colorrendition chart. J. App. Photog. Eng., 2(3):95-99.
Meyer, G.W. 1988. Wavelength selection for synthetic image generation. Comp. Vision, Graphics, and Image Proc., 41:57-79.
Raja, Y., McKenna, S., and Gong, S. 1998. Colour model selection and adaptation in dynamic scenes. In Proceedings European Conference Computer Vision, Freiburg, Germany, pp. 460-474.
Shafer, S.A. 1985. Using color to separate reflection components. Color Res. App., 10(4):210-218.
Stokes, M., Fairchild, M.D., and Berns, R.S. 1992. Precision requirements for digital color reproduction. ACM Transactions on Graphics, 11(4):406-422.
Störring, M., Andersen, H.J., and Granum, E. 1999. Skin colour detection under changing lighting conditions. In Proceedings 7th Symposium on Intelligent Robotics Systems, Coimbra, Portugal, pp. 187-195.
Tominaga, S. 1991. Surface identification using the dichromatic re-flection model. IEEE Trans. PAMI, 13(7):658-670.
Tominaga, S. 1996. A multi-channel vision system for estimating surface and illuminant functions. J. Opt. Soc. Am. A, 13:2163-2173.
Tominaga, S. and Wandell, B.A. 1989. Standard surface-reflectance model and illuminant estimation. Journal of the Optical Society of America A, 6(4):576-584.
Tominaga, S. and Wandell, B.A. 1990. Component estimation of surface spectral reflectance. Journal of the Optical Society of America A, 7(2):312-317.
Tong, F. and Funt, B.V. 1988. Specularity removal for shape from shading. In Proceedings: Vision Interface 1988, Edmonton, Alberta, Canada, pp. 98-103.
Wyszecki, G. and Stiles, W.S. 1982. Color Science: Concepts and Methods, Quantitative Data and Formulas. Wiley: New York, 2nd ed.
Yuille, A. 1987. A method for computing spectral reflectance. Biological Cybernetics, 56:195-201.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Finlayson, G.D., Schaefer, G. Solving for Colour Constancy using a Constrained Dichromatic Reflection Model. International Journal of Computer Vision 42, 127–144 (2001). https://doi.org/10.1023/A:1011120214885
Issue Date:
DOI: https://doi.org/10.1023/A:1011120214885