A method for computing spectral reflectance
Biological Cybernetics
A novel algorithm for color constancy
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color constancy for scenes with varying illumination
Computer Vision and Image Understanding - Special issue on physics-based modeling and reasoning in computer vision
Color by Correlation: A Simple, Unifying Framework for Color Constancy
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color constancy under varying illumination
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Colour Constancy Using the Chromagenic Constraint
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
IEEE Transactions on Image Processing
A comparison of computational color constancy Algorithms. II. Experiments with image data
IEEE Transactions on Image Processing
Hybrid color space transformation to visualize color constancy
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
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This paper introduces a non-uniform filter formulation into the Brainard and Freeman Bayesian color constancy technique. The formulation comprises sensor measurements taken through a non-uniform filter, of spatially-varying spectral sensitivity, placed on the camera lens. The main goal of this paper is twofold. First, it presents a framework in which sensor measurements obtained through a non-uniform filter can be sequentially incorporated into the Bayesian probabilistic formulation. Second, it shows that such additional measurements obtained reduce the effect of the prior in Bayesian color constancy. For the purposes of testing the proposed framework, we use a filter formulation of two portions of different spectral sensitivities. We show through experiments on real data that improvement in the parameter estimation can be obtained inexpensively by sequentially incorporating additional information obtained from the sensor through the different portions of a filter by Bayesian chaining. We also show that our approach outperforms previous approaches in the literature.