A method for computing spectral reflectance
Biological Cybernetics
The Synthesis and Analysis of Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface Identification Using the Dichromatic Reflection Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color Constancy Using the Inter-Reflection from a Reference Nose
International Journal of Computer Vision
Color Space Analysis of Mutual Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Objective Colour from Multispectral Imaging
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
What is the Spectral Dimensionality of Illumination Functions in Outdoor Scenes?
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A sequential Bayesian approach to color constancy using non-uniform filters
Computer Vision and Image Understanding
Challenge: mobile optical networks through visual MIMO
Proceedings of the sixteenth annual international conference on Mobile computing and networking
Illumination invariant color texture analysis based on sum- and difference-histograms
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Integration of 3D and multispectral data for cultural heritage applications: Survey and perspectives
Image and Vision Computing
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A separation algorithm for achieving color constancy and theorems concerning its accuracy are presented. The algorithm requires extra information, over and above the usual three values mapping human cone responses, from the optical system. However, with this additional information-specifically, a sampling across the visible range of the reflected, color-signal spectrum impinging on the optical sensor-the authors are able to separate the illumination spectrum from the surface reflectance spectrum contained in the color-signal spectrum which is, of course, the product of these two spectra. At the heart of the separation algorithm is a general statistical method for finding the best illumination and reflectance spectra, within a space represented by finite-dimensional linear models of statistically typical spectra, whose product closely corresponds to the spectrum of the actual color signal. Using this method, the authors are able to increase the dimensionality of the finite-dimensional linear model for surfaces to a realistic value. One method of generating the spectral samples required for the separation algorithm is to use the chromatic aberration effects of a lens. An example of this is given. The accuracy achieved in a large range of tests is detailed, and it is shown that agreement with actual surface reflectance is excellent.