Estimation of Multiple Illuminants Based on Specular Highlight Detection

  • Yoshie Imai
  • Yu Kato
  • Hideki Kadoi
  • Takahiko Horiuchi
  • Shoji Tominaga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6626)

Abstract

This paper proposes a method for estimating the scene illuminant spectral power distributions of multiple light sources under a complex illumination environment. The spectral power distributions including natural and artificial illuminants are estimated based on the image data from a high-dimensional spectral imaging system. We note that specular highlights on inhomogeneous dielectric object surfaces includes much information about scene illumination according to the dichromatic reflection model. First, we describe several methods for detecting specular highlight areas. We assume a curved object surface illuminated by multiple light sources from different directions. Then we estimate the illuminant spectrum of each light source from the image data of that highlight area. Based on this principle, we present an algorithm to estimate multiple illuminants. The feasibility of the proposed method is shown in experiments.

Keywords

Multiple light sources dichromatic reflection model specular highlight area illuminant estimation 

References

  1. 1.
    Tominaga, S.: Multichannel vision system for estimating surface and illumination functions. J. Optical Society of America A 13(11), 2163–2173 (1996)CrossRefGoogle Scholar
  2. 2.
    Tominaga, S., Wandell, B.A.: Standard surface-reflectance model and illuminant estimation. J. Optical Society of America A 6(4), 576–584 (1986)CrossRefGoogle Scholar
  3. 3.
    Maloney, L.T.: Evaluation of linear models of surface spectral reflectance with small numbers of parameters. J. of the Optical Society of America A 3(10), 1673–1683 (1986)CrossRefGoogle Scholar
  4. 4.
    Maloney, L.T., Wandell, B.A.: Color constancy: a method for recovering surface spectral reflectance. J. Optical Society of America A 3(1), 29–33 (1986)CrossRefGoogle Scholar
  5. 5.
    Tominaga, S., Haraguchi, H.: Estimation of fluorescent scene illuminant by a spectral camera system. In: Color Imaging X: Processing, Hardcopy, and Applications, San Jose, Calif, USA. Proceedings of SPIE, vol. 5667, pp. 128–135 (2005)Google Scholar
  6. 6.
    Tominaga, S.N., Tanaka, N.: Feature article: omnidirectional scene illuminant estimation using a mirrored ball. Journal of Imaging Science and Technology 50(3), 217–227 (2006)CrossRefGoogle Scholar
  7. 7.
    Schultz, S., Doerschner, K., Maloney, L.T.: Color constancy and hue scaling. Journal of Vision 6(10), 1102–1116 (2006)CrossRefGoogle Scholar
  8. 8.
    Tominaga, S.: Estimation of composite daylight-fluorescent light components based on multi-spectral scene images, In: Proceedings of the 14th IS&T/SID Color Imaging Conference, Scottsdale, Ariz, USA, pp.125–130 (2006) Google Scholar
  9. 9.
    van de Weijer, J., Gevers, T., Gijsenij, A.: Edge-based color constancy. IEEE Transactions on Image Processing 16(9), 2207–2214 (2007)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Zhou, W., Kambhamettu, C.: A unified framework for scene illuminant estimation. Image and Vision Computing 26(3), 415–429 (2008)CrossRefGoogle Scholar
  11. 11.
    Klinker, G.J., Shafer, S.A., Kanade, T.: The Measurement of Highlights in Color Images. International Journal of Computer Vision 2(1), 7–26 (1992)CrossRefGoogle Scholar
  12. 12.
    Tan, R.T., Nishino, K., Ikeuchi, K.: Separating Reflection Components Based on Chromaticity and Noise Analysis. IEEE Transaction on Pattern Analysis and Machine Intelligence 26(10), 1373–1379 (2004)CrossRefGoogle Scholar
  13. 13.
    Xu, S.C., Ye, X., Wu, Y., Zhang, S.: Highlight detection and removal based on chromaticity. In: Kamel, M.S., Campilho, A.C. (eds.) ICIAR 2005. LNCS, vol. 3656, pp. 199–206. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  14. 14.
    Angelopoulou, E.: Specular Highlight Detection Based on the Fresnel Reflection Coefficient. In: IEEE 11th International Conference on Computer Vision, pp. 1–8 (2007)Google Scholar
  15. 15.
    Buchsbaum, G.: A spatial processor model for object colour perception. J. Franklin Institute 310(1), 1–26 (1980)CrossRefGoogle Scholar
  16. 16.
    Tominaga, S., Kimachi, A.: Polarization imaging for material classification. Optical Engineering 47(12), 123201 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yoshie Imai
    • 1
    • 2
  • Yu Kato
    • 1
  • Hideki Kadoi
    • 1
  • Takahiko Horiuchi
    • 1
  • Shoji Tominaga
    • 1
  1. 1.Graduate School of Advanced Integration ScienceChiba UniversityJapan
  2. 2.Toshiba CorporationJapan

Personalised recommendations