Towards On-Board Color Constancy on Mobile Robots
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
Gamut Constrained Illuminant Estimation
International Journal of Computer Vision
A survey of skin-color modeling and detection methods
Pattern Recognition
Light mixture estimation for spatially varying white balance
ACM SIGGRAPH 2008 papers
Adaptive Recognition of Color-Coded Objects in Indoor and Outdoor Environments
RoboCup 2007: Robot Soccer World Cup XI
Color learning and illumination invariance on mobile robots: A survey
Robotics and Autonomous Systems
MIRAGE '09 Proceedings of the 4th International Conference on Computer Vision/Computer Graphics CollaborationTechniques
Generalized Gamut Mapping using Image Derivative Structures for Color Constancy
International Journal of Computer Vision
Improved color acquisition and mapping on 3D models via flash-based photography
Journal on Computing and Cultural Heritage (JOCCH)
A Solution of the Dichromatic Model for Multispectral Photometric Invariance
International Journal of Computer Vision
Expert Systems with Applications: An International Journal
Illumination independent object recognition
RoboCup 2005
Singular value decomposition-based illumination compensation in video
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
A new color constancy algorithm based on the histogram of feasible mappings
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Towards illumination invariance in the legged league
RoboCup 2004
A flexible auto white balance based on histogram overlap
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
Simultaneous image color correction and enhancement using particle swarm optimization
Engineering Applications of Artificial Intelligence
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The color constancy problem, that is, estimating the color of the scene illuminant from a set of image data recorded under an unknown light, is an important problem in computer vision and digital photography. The gamut mapping approach to color constancy is, to date, one of the most successful solutions to this problem. In this algorithm the set of mappings taking the image colors recorded under an unknown illuminant to the gamut of all colors observed under a standard illuminant is characterized. Then, at a second stage, a single mapping is selected from this feasible set. In the first version of this algorithm Forsyth (1990) mapped sensor values recorded under one illuminant to those recorded under a second, using a three-dimensional (3-D) diagonal matrix. However because the intensity of the scene illuminant cannot be recovered Finlayson (see IEEE Trans. Pattern Anal. Machine Intell. vol.18, no.10, p.1034-38, 1996) modified Forsyth's algorithm to work in a two-dimensional (2-D) chromaticity space and set out to recover only 2-D chromaticity mappings. While the chromaticity mapping overcomes the intensity problem it is not clear that something has not been lost in the process. The first result of this paper is to show that only intensity information is lost. Formally, we prove that the feasible set calculated by Forsyth's original algorithm, projected into 2-D, is the same as the feasible set calculated by the 2-D algorithm. Thus, there is no advantage in using the 3-D algorithm and we can use the simpler, 2-D version of the algorithm to characterize the set of feasible illuminants. Another problem with the chromaticity mapping is that it is perspective in nature and so chromaticities and chromaticity maps are perspectively distorted. Previous work demonstrated that the effects of perspective distortion were serious for the 2-D algorithm. Indeed, in order to select a sensible single mapping from the feasible set this set must first be mapped back up to 3-D. We extend this work to the case where a constraint on the possible color of the illuminant is factored into the gamut mapping algorithm. We show here that the illumination constraint can be enforced during selection without explicitly intersecting the two constraint sets. In the final part of this paper we reappraise the selection task. Gamut mapping returns the set of feasible illuminant maps. Our new algorithm is tested using real and synthetic images. The results of these tests show that the algorithm presented delivers excellent color constancy