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Consistent Surface Color for Texturing Large Objects in Outdoor Scenes

  • Rei Kawakami
  • Robby T. Tan
  • Katsushi Ikeuchi

Color appearance of an object is significantly influenced by the color of the illumination. When the illumination color changes, the color appearance of the object will change accordingly, causing its appearance to be inconsistent. To arrive at color constancy, we have developed a physics-based method of estimating and removing the illumination color. In this chapter, we focus on the use of this method to deal with outdoor scenes, since very few physics-based methods have successfully handled outdoor color constancy. Our method is principally based on shadowed and non-shadowed regions. Previously researchers have discovered that shadowed regions are illuminated by sky light, while non-shadowed regions are illuminated by a combination of sky light and sunlight. Based on this difference of illumination, we estimate the illumination colors (both the sunlight and the sky light) and then remove them. To reliably estimate the illumination colors in outdoor scenes, we include the analysis of noise, since the presence of noise is inevitable in natural images. As a result, compared to existing methods, the proposed method is more effective and robust in handling outdoor scenes. In addition, the proposed method requires only a single input image, making it useful for many applications of computer vision.

Keywords

Optic Society Error Ratio Color Constancy Shadowed Region Color Appearance 
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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References

  1. [1]
    K. Barnard, F. Ciurea, and B. Funt. Sensor sharpening for computational color constancy. Journal of Optics Society of America A., 18(11):2728-2743,2001.CrossRefGoogle Scholar
  2. [2]
    K. Barnard, G. Finlayson, and B. Funt. Color constancy for scenes with varying illumination. Computer Vision and Image Understanding, 65 (2):311-321, 1997.CrossRefGoogle Scholar
  3. [3]
    D.H. Brainard and W.T. Freeman. Bayesian color constancy. Journal of Optics Society of America A., 14(7):1393-1411, 1997.CrossRefGoogle Scholar
  4. [4]
    M. D’Zmura. Color constancy: surface color from changing illumination. Journal of Optics Society of America A., 9(3):490-493, 1992.CrossRefGoogle Scholar
  5. [5]
    M. D’Zmura and P. Lennie. Mechanism of color constancy. Journal of Optics Society of America A., 3(10):1162-1672, 1986.Google Scholar
  6. [6]
    G.D. Finlayson, M.S. Drew, and B.V. Funt. Spectral sharpening sensor transformations for improved color constancy. Journal of Optics Society of America A., 11(10):1162-1672, 1994.Google Scholar
  7. [7]
    G.D. Finlayson and B.V. Funt. Color constancy using shadows. Perception, 23:89-90, 1994.Google Scholar
  8. [8]
    G.D. Finlayson, B.V. Funt, and K. Barnard. Color constancy under varying illumination. in proceeding of IEEE International Conference on Computer Vision, pages 720-725, 1995.Google Scholar
  9. [9]
    G.D. Finlayson, S.D. Hordley, and P.M. Hubel. Color by correlation: a simple, unifying, framework for color constancy. IEEE Trans. on Pattern Analysis and Machine Intelligence, 23(11):1209-1221, 2001.CrossRefGoogle Scholar
  10. [10]
    G.D. Finlayson and S.D. Hordley. Color constancy at a pixel. Journal of Optics Society of America A., 18(2):253-264, 2001.CrossRefGoogle Scholar
  11. [11]
    B.V. Funt, M. Drew, and J. Ho. Color constancy from mutual reflection. International Journal of Computer Vision, 6(1):5-24, 1991.CrossRefGoogle Scholar
  12. [12]
    J.M. Geusebroek, R. Boomgaard, S. Smeulders, and T. Gevers. A physical basis for color constancy. In The First European Conference on Colour in Graphics, Image and Vision, pages 3-6, 2002.Google Scholar
  13. [13]
    D.B. Judd, D.L. MacAdam, and G. Wyszecky. Spectral distribution of typical daylight as a function of correlated color temperature. Journal of Optics Society of America, 54(8):1031-1040, 1964.CrossRefGoogle Scholar
  14. [14]
    E.H. Land and J.J. McCann. Lightness and retinex theory. Journal of Optics Society of America, 61(1):1-11, 1971.CrossRefGoogle Scholar
  15. [15]
    H.C. Lee. Method for computing the scene-illuminant from specular highlights. Journal of Optics Society of America A., 3(10):1694-1699, 1986.CrossRefGoogle Scholar
  16. [16]
    H.C. Lee. Illuminant color from shading. In Perceiving, Measuring and Using Color, page 1250, 1990.Google Scholar
  17. [17]
    R. T. Tan, K. Nishino, and K. Ikeuchi. Color constancy through inverse intensity-chromaticity space. Journal of the Optical Society of America A (JOSA A), 21(3):321-334, 2004.CrossRefGoogle Scholar
  18. [18]
    S. Tominaga and B.A. Wandell. Natural scene-illuminant estimation using the sensor correlation. Proceedings of the IEEE, 90(1):42-56, 2002.CrossRefGoogle Scholar
  19. [19]
    J.A. Marchant and C.M. Onyango. Shadow-invariant classification for scenes illuminated by daylight. Journal of Optics Society of America A. 17 (11):1952-1961, 2000.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Rei Kawakami
  • Robby T. Tan
  • Katsushi Ikeuchi
    • 1
  1. 1.Institute of Industrial ScienceThe University of TokyoMeguro-kuJapan

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