Abstract
In his paper we introduce two improvements to the threedimensional gamut mapping approach to computational colour constancy. This approach consist of two separate parts. First the possible solutions are constrained. This part is dependent on the diagonal model of illumination change, which in turn, is a function of the camera sensors. In this work we propose a robust method for relaxing this reliance on the diagonal model. The second part of the gamut mapping paradigm is to choose a solution from the feasible set. Currently there are two general approaches for doing so. We propose a hybrid method which embodies the benefits of both, and generally performs better than either. We provide results using both generated data and a carefully calibrated set of 321 images. In the case of the modification for diagonal model failure, we provide synthetic results using two cameras with a distinctly different degree of support for the diagonal model. Here we verify that the new method does indeed reduce error due to the diagonal model. We also verify that the new method for choosing the solution offers significant improvement, both in the case of synthetic data and with real images.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
D. Forsyth, A novel algorithm for color constancy, International Journal of Computer Vision, 5, pp. 5–36 (1990).
K. Barnard, Computational colour constancy: taking theory into practice, M.Sc. Thesis, Simon Fraser University, School of Computing (1995).
B. Funt, K. Barnard, and L. Martin, Is Colour Constancy Good Enough?, Proc. 5th European Conference on Computer Vision, pp. I:445–459 (1998).
K. Barnard, Practical colour constancy, Ph.D. Thesis, Simon Fraser University, School of Computing Science (1999), ftp://fas.sfu.ca/pub/cs/theses/1999/KobusBarnardPhD.ps.gz
G. D. Finlayson, M. S. Drew, and B. V. Funt, Spectral Sharpening: Sensor Transformations for Improved Color Constancy, Journal of the Optical Society of America A, 11, pp. 1553–1563 (1994).
K. Barnard and B. Funt, Experiments in Sensor Sharpening for Color Constancy, Proc. IS&T/SID Sixth Color Imaging Conference: Color Science, Systems and Applications, pp. 43–46 (1998).
K. Barnard, Color constancy with fluorescent surfaces, Proc. IS&T/SID Seventh Color Imaging Conference: Color Science, Systems and Applications (1999).
G. D. Finlayson, Coefficient Color Constancy,: Simon Fraser University, School of Computing (1995).
G. D. Finlayson, Color in perspective, IEEE Transactions on Pattern Analysis and Machine Intelligence, 18, pp. 1034–1038 (1996).
G. Finlayson and S. Hordley, A theory of selection for gamut mapping colour constancy, Proc. IEEE Conference on Computer Vision and Pattern Recognition (1998).
G. Finlayson and S. Hordley, Selection for gamut mapping colour constancy, Proc. British Machine Vision Conference (1997).
J. A. Worthey, Limitations of color constancy, Journal of the Optical Society of America [Suppl.], 2, pp. 1014–1026 (1985).
J. A. Worthey and M. H. Brill, Heuristic analysis of von Kries color constancy, Journal of the Optical Society of America A, 3, pp. 1708–1712 (1986).
P. L. Vora, J. E. Farrell, J. D. Tietz, and D. H. Brainard, Digital color cameras—Spectral response, (1997), available from http://color.psych.ucsb.edu/hyperspectral/..
E. L. Krinov, Spectral Reflectance Properties of Natural Formations: National Research Council of Canada, 1947.
J. L. Devore, Probability and Statistics for Engineering and the Sciences. Monterey, CA: Brooks/Cole Publishing Company, 1982.
B. Funt, V. Cardei, and K. Barnard, Learning Color Constancy, Proc. IS&T/SID Fourth Color Imaging Conference: Color Science, Systems and Applications, pp. 58–60 (1996).
V. Cardei, B. Funt, and K. Barnard, Modeling color constancy with neural networks, Proc. International Conference on Vision Recognition, Action: Neural Models of Mind and Machine (1997).
V. Cardei, B. Funt, and K. Barnard, Adaptive Illuminant Estimation Using Neural Networks, Proc. International Conference on Artificial Neural Networks (1998).
G. D. Finlayson, P. H. Hubel, and S. Hordley, Color by Correlation, Proc. IS&T/SID Fifth Color Imaging Conference: Color Science, Systems and Applications, pp. 6–11 (1997).
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Barnard, K. (2000). Improvements to Gamut Mapping Colour Constancy Algorithms. In: Computer Vision - ECCV 2000. ECCV 2000. Lecture Notes in Computer Science, vol 1842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45054-8_26
Download citation
DOI: https://doi.org/10.1007/3-540-45054-8_26
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67685-0
Online ISBN: 978-3-540-45054-2
eBook Packages: Springer Book Archive