EIGENVECTOR ANALYSIS FOR OPTIMAL FILTERING UNDER DIFFERENT LIGHT SOURCES

Juha Lehtonen, Jussi Parkkinen, Timo Jaaskelainen, Alexei Kamshilin

2009

Abstract

Eigenvectors from Standard Object Colour Spectra (SOCS) set were used with several other spectra sets to find the optimal sampling intervals for optimal number of eigenvectors. The sampling intervals were calculated for each eigenvector separately. The analysis was applied not only for different sets of reflectance spectra, but also for spectra sets under different real light sources and standard illuminations. It is shown that 20 nm sampling interval for eigenvectors from SOCS set can be used for reflectance data and data under such light sources which spectrum is smooth. However, data under peaky real fluorescent light sources and standard F-illuminant require accurate 5 nm or even narrower sampling interval for the first few eigenvectors, but can be wider with some of the others. These eigenvectors from SOCS set are shown to be applicable for the other data sets. The results give guidelines for the required accuracy of eigenvectors under different light sources that can be considered e.g. in eigenvector-based filter design.

References

  1. Bonnardel V., and Maloney, L., 2000. Daylight, biochrome surfaces and human chromatic response in the Fourier domain, J. Opt. Soc. Am. A 17(4), 677- 686.
  2. Buchsbaum, G., and Gottschalk, A., 1984. Chromacity coordinates of frequency-limited functions, J. Opt. Soc. Am. A 1(8), 885-887.
  3. Early, E., and Nadal, M., 2004. Uncertainty analysis for reflectance colorimetry, Color Res. Appl. 29(3), 205- 216.
  4. Fairman, H., 1985. The calculation of weight factors for tristimulus integration, Color Res. Appl. 10(4), 199- 203.
  5. Farnsworth, D., 1957. The Farnsworth-Munsell 100-Hue Test, Munsell Color Company. Baltimore, MD, revised ed.
  6. Funt, B., and Lewis, B., 2000. Diagonal versus affine transformations for color correction, J. Opt. Soc. Am. A 17(11), 2108-2112.
  7. Hauta-Kasari, M., Lehtonen, J., Parkkinen, J., and Jaaskelainen, T., 2006. Image Format for Spectral Image Browsing, J. Imag. Sci. Tech. 50(6), 572-582.
  8. Hauta-Kasari, M., Wang, W., Toyooka, S., Parkkinen, J., and Lenz, R., 1998. Unsupervised Filtering of Munsell Spectra, Proc. of the Third Asian Conference on Computer Vision, Vol. I, Hong Kong, 248-255.
  9. Hernández-Andrés, J., Romero, J., and Lee Jr., R., 2001. Colorimetric and spectroradiometric characteristics of narrow-field-of-view clear skylight in Granada, Spain, J. Opt. Soc. Am. A 18(2), 412-420.
  10. Hyvärinen, T., Herrala, E., and Dall'Ava, A., 1999. Direct sight imaging spectrograph: A unique add-on component brings spectral imaging to industrial applications, Proc. SPIE 3740, 468-471.
  11. Hyvärinen, A., Karhunen, J., and Oja, E., 2001. Independent Component Analysis, Wiley.
  12. Jaaskelainen, T., Silvennoinen, R., Hiltunen, J., and Parkkinen, J., 1994. Classification of the reflectance spectra of pine, spruce and birch, Appl. Opt. 33, 2356- 2362.
  13. Japanese Standards Association, 1998. Standard object colour spectra database for colour reproduction evaluation, TR X 0012.
  14. Kohonen, O., Parkkinen, J., and Jaaskelainen, T., 2006. Databases for Spectral Color Science, Color Res. Appl. 31(5), 2006, 381-390.
  15. Lehtonen, J., Parkkinen, J., and Jaaskelainen, T., 2006. Optimal Sampling of Color Spectra, J. Opt. Soc. Am. A 23(12), 2983-2988.
  16. Maloney, L., 1986. Evaluation of linear models of surface spectral reflectance with small number of parameters, J. Opt. Soc. Am. A 3(10), 1673-1683.
  17. Martinez, K., Cupitt, J., Saunders, D., and Pillay, R., 2002. Ten years of art imaging research, Proc. IEEE 90(1), 28-41.
  18. Morovic, P., 2002. Metamer sets, Ph.D. dissertation, University of East Anglia.
  19. Nishibori, M., 2002. Problems and solutions in medical color imaging, Proc. of Second International Symposium on Multispectral Imaging and High Accurate Color Reproduction, Chiba, Japan, 2002, 9- 17.
  20. Ohta, N., and Robertson, A., 2005. Colorimetry Fundamentals and Applications, Wiley.
  21. Pantone, 2008. http://www.pantone.com.
  22. Parkkinen, J., Hallikainen, J., and Jaaskelainen, T., 1989. Characteristic spectra of Munsell colors, J. Opt. Soc. Am. A 6(2), 318-322.
  23. Piché, R., 2002. Nonnegative color spectrum analysis filters from principal component analysis characteristic spectra, J. Opt. Soc. Am. A 19(10), 1946-1950.
  24. Sándor, N., Ondró, T., and Schanda, J., 2005. Spectral Interpolation Errors, Color Res. Appl. 30(5), 348-353.
  25. Schettini, R., 1994. Deriving spectral reflectance functions of computer-simulated object colours, Comput. Graph. Forum 13(4), 211-217.
  26. University of Joensuu Color Group, 2008. Spectral Database, http://spectral.joensuu.fi.
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Paper Citation


in Harvard Style

Lehtonen J., Parkkinen J., Jaaskelainen T. and Kamshilin A. (2009). EIGENVECTOR ANALYSIS FOR OPTIMAL FILTERING UNDER DIFFERENT LIGHT SOURCES . In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 95-100. DOI: 10.5220/0001798700950100


in Bibtex Style

@conference{visapp09,
author={Juha Lehtonen and Jussi Parkkinen and Timo Jaaskelainen and Alexei Kamshilin},
title={EIGENVECTOR ANALYSIS FOR OPTIMAL FILTERING UNDER DIFFERENT LIGHT SOURCES},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={95-100},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001798700950100},
isbn={978-989-8111-69-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)
TI - EIGENVECTOR ANALYSIS FOR OPTIMAL FILTERING UNDER DIFFERENT LIGHT SOURCES
SN - 978-989-8111-69-2
AU - Lehtonen J.
AU - Parkkinen J.
AU - Jaaskelainen T.
AU - Kamshilin A.
PY - 2009
SP - 95
EP - 100
DO - 10.5220/0001798700950100