Affine Illumination Compensation for Multispectral Images

  • Pedro Latorre Carmona
  • Reiner Lenz
  • Filiberto Pla
  • Jose M. Sotoca
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)


We apply a general form of affine transformation model to compensate illumination variations in a series of multispectral images of a static scene and compare it to a particular affine and a diagonal transformation models. These models operate in the original multispectral space or in a lower-dimensional space obtained by Singular Value Decomposition (SVD) of the set of images. We use a system consisting of a multispectral camera and a light dome that allows the measurement of multispectral data under carefully controlled illumination conditions to generate a series of multispectral images of a static scene under varying illumination conditions. We evaluate the compensation performance using the CIELAB colour difference between images. The experiments show that the first 2 models perform satisfactorily in the original and lower dimensional spaces.


Transformation Model Multispectral Image Illumination Change Lower Dimensional Space Color Constancy 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Pedro Latorre Carmona
    • 1
  • Reiner Lenz
    • 2
  • Filiberto Pla
    • 1
  • Jose M. Sotoca
    • 1
  1. 1.Depto. Lenguajes y Sistemas Informáticos, Universidad Jaume I, Campus del Riu Sec s/n, 12071, Castellón de la PlanaSpain
  2. 2.Department of Science and Technology, Linköping University, Campus Norrköping, NorrköpingSweden

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