The variational approach to shape from shading
Computer Vision, Graphics, and Image Processing
The Synthesis and Analysis of Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Solving for Colour Constancy using a Constrained Dichromatic Reflection Model
International Journal of Computer Vision
A Variational Framework for Retinex
International Journal of Computer Vision
A Theory of Multiplexed Illumination
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Multi-Spectral Imaging by Optimized Wide Band Illumination
International Journal of Computer Vision
A Solution of the Dichromatic Model for Multispectral Photometric Invariance
International Journal of Computer Vision
Uncontrolled modulation imaging
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Practical spectral characterization of trichromatic cameras
Proceedings of the 2011 SIGGRAPH Asia Conference
Bayesian Reasoning and Machine Learning
Bayesian Reasoning and Machine Learning
Image capture: simulation of sensor responses from hyperspectral images
IEEE Transactions on Image Processing
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In this paper, we aim at learning the colour matching functions making use of hyperspectral and trichromatic imagery. The method presented here is quite general in nature, being data driven and devoid of constrained setups. Here, we adopt a probabilistic formulation so as to recover the colour matching functions directly from trichromatic and hyperspectral pixel pairs. To do this, we derive a log-likelihood function which is governed by both, the spectra-to-colour equivalence and a generative model for the colour matching functions. Cast into a probabilistic setting, we employ the EM algorithm for purposes of maximum a posteriori inference, where the M-step is effected making use of Levenberg-Marquardt optimisation. We present results on real-world data and provide a quantitative analysis based upon a colour calibration chart.