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.


Hyperspectral Image Colour Match Image Radiance Colour Match Function Liquid Crystal Tunable Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Luis Romero-Ortega
    • 1
  • Antonio Robles-Kelly
    • 1
    • 2
    • 3
  1. 1.School of Eng. and Inf. Tech.UNSW@ADFACanberraAustralia
  2. 2.NICTACanberraAustralia
  3. 3.Research School of Eng.ANUCanberraAustralia

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