Computational geometry: an introduction
Computational geometry: an introduction
A novel algorithm for color constancy
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color by Correlation: A Simple, Unifying Framework for Color Constancy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Colour by Correlation in a Three-Dimensional Colour Space
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Improvements to Gamut Mapping Colour Constancy Algorithms
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Latest results in digital color film restoration
Machine Graphics & Vision International Journal - Special issue on latest results in colour image processing and applications
Example-based color transformation for image and video
GRAPHITE '05 Proceedings of the 3rd international conference on Computer graphics and interactive techniques in Australasia and South East Asia
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Gamut mapping colour constancy attempts to determine the set of diagonal matrices taking the gamut of image colours under an unknown illuminantion to the gamut of colours observed under a standard illuminant. Forsyth [5] developed such an algorithm in rgb sensor space which Finlayson [3] later modified to work in a 2-d chromaticity space. In this paper we prove that Forsyth's 3-d solution gamut is, when projected to 2-d, identical to the gamut recovered by the 2-d algorithm. Whilst this implies that there is no inherent disadvantage in working in chromaticity space, this algorithm has a number of problems; the 2-d solution set is distorted and contains practically non-feasible illuminants. These problems have been addressed separately in previous work [4, 3]; we address them together in this paper.Non-feasible illuminants are discarded by intersecting the solution gamut with a non-convex gamut of common illuminants. In 2-d this intersection is relatively simple, but to remove the distortion, both these sets should be represented as 3-d cones of mappings, and the intersection is more difficult. We present an algorithm which avoids performing this intersection explicitly and which is simple to implement. Tests of this algorithm on both real and synthetic images show that it performs significantly better than the best current algorithms.