Color linear model

  • Chang-Yeong] Kim
  • Yang-Seok Seo
  • In-So Kweon
Poster Session A: Color & Texture, Enhancement, Image Analysis & Pattern Recognition, Segmentation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1310)


In this paper, procedures for creating an effective linear model to represent surface spectra are presented. The model is derived by considering spectral data and the human visual characteristic that depends on wave lengths. Two human visual weighting functions (HVWF) are derived from human visual characteristic. The basis functions of the linear model for the surface reflectance are selected by minimizing least square error in approximating the spectral data weighted by the HVWF. The linear model is shown to perform better than conventional linear models for color constancy, the surface identification related to object recognition, and the characterization of a scanner and a camera.


  1. 1.
    J. Cohen, Dependency of the spectral reflectance curves of Munsell Color chips, Psychnomic Sci. 1, 367–370 (1964).Google Scholar
  2. 2.
    J. Parkkinen, J. Hallikainen, and T, Jaaskelainen, Characteristic spectra of Munsell colors, J. Opt. Soc. Am. A, 6, 318–322 (1989).Google Scholar
  3. 3.
    L. T. Maloney, Evaluation of linear models of surface spectral reflectance with small numbers of parameters, Color Res. & Appl. 14, 325–334 (1986).Google Scholar
  4. 4.
    L. T. Maloney and B. A. Wandell, Color constancy: A method for recovering surface spectral reflectance, J. Opt. Soc. Am. A, 3, 29–33 (1986).Google Scholar
  5. 5.
    M. J. Vrhel and H. J. Trussell, Color Correction using principal components, Color Res. & Appl., 17, 328–338 (1992).Google Scholar
  6. 6.
    D. Marimont and B. A. Wandell, Linear models of surface and illuminant spectra, J. Opt. Soc. Am. A, Vol9, No. 11. Nov., 1905–1913 (1992).Google Scholar
  7. 7.
    M. J. Vrhel, R. Gershon, and L. S. Iwan, Measurement and Analysis of Object reflectance Spectra, Color Res. & Appl. 19, 4–9 (1994).Google Scholar
  8. 8.
    G. Wyszecki and W. S. Stiles, Color Science 2nd Ed., John Wiley & Sons (1982).Google Scholar
  9. 9.
    W.H. Press, S.A. Teukolsky, W.T. Vetterling and B.P. Flannery, Numerical Recipes in C-The Art of Scientific Computing, Cambridge Univ. Press (1992).Google Scholar
  10. 10.
    S. D. Lee, C. Y. Kim, and Y. S. Seo, Linear Model of Surface and Scanner Characterization Method, in IS&T/SPIE's Symposium on Electronic Imaging: Device Independent Color Imaging II, Feb., San Jose, California, 84–93 (1995).Google Scholar
  11. 11.
    T. Jaakelainen, J. Parkkinen, and S. Toyooka, Vector-subspace Model for color representation, J. Opt. Soc. Am. A, Vol. 7, No. 4, April,725–730 (1990).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Chang-Yeong] Kim
    • 1
  • Yang-Seok Seo
    • 1
  • In-So Kweon
    • 2
  1. 1.Signal Processing Lab. Samsung Advanced Institute of TechnologySuwonKorea
  2. 2.Dept. of Electrical EngineeringKorea Advanced Institute of Science and TechnologySeoulKorea

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