Advertisement

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)

Abstract

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.

Keywords

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.

References

  1. 1.
    Barnard, K., Finlayson, G., Funt, B.: Color constancy for scenes with varying illumination. Computer Vision and Image Understanding 65, 311–321 (1997)CrossRefGoogle Scholar
  2. 2.
    Begelfor, E., Werman, M.: Affine invariance revisited. In: IEEE Conf. on Computer Vision and Pat. Rec., vol. 2, pp. 2087–2094 (2006)Google Scholar
  3. 3.
    Method of measuring and specifying colour rendering properties of light sources. CIE-Technical Report 13.3 (1995)Google Scholar
  4. 4.
    Finlayson, G.D., Drew, M.S., Funt, B.V.: Spectral sharpening: sensor transformations for improved color constancy. Journal of the Opt. Soc. of America, A 11, 1553–1563 (1994)Google Scholar
  5. 5.
    Finlayson, G., Chatterjee, S.S., Funt, B.V.: Color Angular Indexing. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1065, pp. 16–27. Springer, Heidelberg (1996)Google Scholar
  6. 6.
    Finlayson, G.D., Hordley, S.D., Xu, R.: Convex programming colour constancy with a diagonal-offset model. In: IEEE Int. Conf. on Image Processing, vol. 3, pp. 948–951 (2005)Google Scholar
  7. 7.
    Healey, G., Slater, D.: Global color constancy: recognition of objects by use of illumination-invariant properties of color distributions. Journal of the Opt. Soc. of America, A 11, 3003–3010 (1994)CrossRefGoogle Scholar
  8. 8.
    Healey, G., Slater, D.: Computing illumination-invariant descriptors of spatially filtered color image regions. IEEE Trans. on Image Proc. 6, 1002–1013 (1997)CrossRefGoogle Scholar
  9. 9.
    Healey, G., Jain, A.: Retrieving multispectral satellite images using physics-based invariant representations. IEEE Trans. on Pat. Analysis and Mach. Intel. 18, 842–848 (1996)CrossRefGoogle Scholar
  10. 10.
    Heikkilä, J.: Pattern Matching with Affine Moment Descriptors. Pattern Recognition 37, 1825–1834 (2004)CrossRefzbMATHGoogle Scholar
  11. 11.
    Hunt, R.W.G.: Measuring Colour. Fountain Press, Amenia (1998)Google Scholar
  12. 12.
    Lenz, R., Tran, L.V., Meer, P.: Moment based normalization of color images. In: IEEE 3rd Workshop on Multimedia Signal Processing, pp. 103–108 (1998)Google Scholar
  13. 13.
    Schonemann, P.H.: A generalized solution of the orthogonal Procrustes problem. Psychometrika 31, 1–10 (1966)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Solli, M., Andersson, M., Lenz, R., Kruse, B.: Color measurements with a consumer digital camera using spectral estimation techniques. In: Kalviainen, H., Parkkinen, J., Kaarna, A. (eds.) SCIA 2005. LNCS, vol. 3540, pp. 105–114. Springer, Heidelberg (2005)Google Scholar
  15. 15.
    Sprinzak, J., Werman, M.: Affine Point Matching. Pat. Rec. Letters 15, 337–339 (1994)CrossRefGoogle Scholar
  16. 16.
    Wyszecki, G., Stiles, W.S.: Color Science: concepts and methods, quantitative data and formulae. Wiley Series in Pure & Applied Optics. Wiley, Chichester (2000)Google Scholar

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

Personalised recommendations