Marc Ebner


Light which is measured by retinal receptors varies with the illuminant. However, a human observer is able to discount the illuminant and to accurately determine the color of objects. The human brain computes a color constant descriptor which is approximately independent of the illuminant. This ability is called color constancy. Recently, it has been shown that color constancy improves for a moving stimulus. It has been argued that high level motion areas may have an influence on the computation of a color constant descriptor. We have developed a computational model for color perception which can be mapped to the different stages of the human visual system. We test our model with two types of stimuli: stationary and moving. In our model, color constancy is computed purely bottom up. Our model also shows better color constancy for a moving stimulus. This indicates that an influence from high level motion areas is not required.


  1. Barnard, K., Finlayson, G., and Funt, B. (1997). Color constancy for scenes with varying illumination. Computer Vision and Image Understanding, 65(2):311-321.
  2. Blake, A. (1985). Boundary conditions for lightness computation in mondrian world. Computer Vision, Graphics, and Image Processing, 32:314-327.
  3. Buchsbaum, G. (1980). A spatial processor model for object colour perception. Journal of the Franklin Institute, 310(1):337-350.
  4. Dartnall, H. J. A., Bowmaker, J. K., and Mollon, J. D. (1983). Human visual pigments: microspectrophotometric results from the eyes of seven persons. Proc. R. Soc. Lond. B, 220:115-130.
  5. Dufort, P. A. and Lumsden, C. J. (1991). Color categorization and color constancy in a neural network model of V4. Biological Cybernetics, 65:293-303.
  6. D'Zmura, M. and Lennie, P. (1986). Mechanisms of color constancy. Journal of the Optical Society of America A, 3(10):1662-1672.
  7. Ebner, M. (2007a). Color Constancy. John Wiley & Sons, England.
  8. Ebner, M. (2007b). How does the brain arrive at a color constant descriptor? In Mele, F., Ramella, G., Santillo, S., and Ventriglia, F., eds., Proc. of the 2nd Int. Symp. on Brain, Vision and Artificial Intelligence, Naples, Italy, pp. 84-93, Berlin. Springer.
  9. Ebner, M. (2009). Color constancy based on local space average color. Machine Vision and Applications Journal, 20(5):283-301.
  10. Ebner, M., Tischler, G., and Albert, J. (2007). Integrating color constancy into JPEG2000. IEEE Transactions on Image Processing, 16(11):2697-2706.
  11. Faugeras, O. D. (1979). Digital color image processing within the framework of a human visual model. IEEE Trans. on, ASSP-27(4):380-393.
  12. Forsyth, D. A. (1990). A novel algorithm for color constancy. Int. J. of Computer Vision, 5(1):5-36.
  13. Funt, B., Ciurea, F., and McCann, J. (2004). Retinex in MATLAB. Journal of Electronic Imaging, 13(1):48- 57.
  14. Herault, J. (1996). A model of colour processing in the retina of vertebrates: From photoreceptors to colour opposition and colour constancy phenomena. Neurocomputing, 12:113-129.
  15. Horn, B. K. P. (1974). Determining lightness from an image. Comp. Graphics and Image Processing, 3:277- 299.
  16. Hunt, R. W. G. (1957). Light energy and brightness sensation. Nature, 179:1026-1027.
  17. International Commission on Illumination (1996). orimetry, 2nd ed., Tech. Report 15.2.
  18. Land, E. H. (1974). The retinex theory of colour vision. Proc. Royal Inst. Great Britain, 47:23-58.
  19. Land, E. H. and McCann, J. J. (1971). Lightness and retinex theory. J. of the Optical Society of America, 61(1):1- 11.
  20. Livingstone, M. S. and Hubel, D. H. (1984). Anatomy and physiology of a color system in the primate visual cortex. The Journal of Neuroscience, 4(1):309-356.
  21. Maloney, L. T. and Wandell, B. A. (1986). Color constancy: a method for recovering surface spectral reflectance. J. of the Optical Society of America A, 3(1):29-33.
  22. McCann, J. J., McKee, S. P., and Taylor, T. H. (1976). Quantitative studies in retinex theory. Vision Res., 16:445-458.
  23. Moore, A., Allman, J., and Goodman, R. M. (1991). A realtime neural system for color constancy. IEEE Transactions on Neural Networks, 2(2):237-247.
  24. Tovée, M. J. (1996). An introduction to the visual system. Cambridge University Press, Cambridge.
  25. Werner, A. (2007). Color constancy improves, when an object moves: High-level motion influences color perception. Journal of Vision, 7(14):1-14.
  26. Zeki, S. (1993). A Vision of the Brain. Blackwell Science, Oxford.
  27. Zeki, S. and Bartels, A. (1999). The clinical and functional measurement of cortical (in)activity in the visual brain, with special reference to the two subdivisions (V4 and V4a) of the human colour centre. Proc. R. Soc. Lond. B, 354:1371-1382.
  28. Zeki, S. and Marini, L. (1998). Three cortical stages of colour processing in the human brain. Brain, 121:1669-1685.

Paper Citation

in Harvard Style

Ebner M. (2012). WHY COLOR CONSTANCY IMPROVES FOR MOVING OBJECTS . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012) ISBN 978-989-8425-89-8, pages 193-198. DOI: 10.5220/0003711301930198

in Bibtex Style

author={Marc Ebner},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012)},

in EndNote Style

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012)
SN - 978-989-8425-89-8
AU - Ebner M.
PY - 2012
SP - 193
EP - 198
DO - 10.5220/0003711301930198