Abstract

Knowledge of the scene illuminant spectral power distribution is useful for many imaging applications, such as color image reproduction and automatic algorithms for image database applications. In many applications accurate spectral characterization of the illuminant is impossible because the input device acquires only three spectral samples. In such applications it is sensible to set a more limited objective of classifying the illuminant as belonging to one of several likely types. We describe a data set of natural images with measured illuminants for testing illuminant classification algorithms. One simple type of algorithm is described and evaluated by using the new data set. The empirical measurements show that illuminant information is more reliable in bright regions than in dark regions. Theoretical predictions of the algorithm’s classification performance with respect to scene illuminant blackbody color temperature are tested and confirmed by using the natural-image data set.

© 2001 Optical Society of America

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References

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  1. The image data used in this paper will be available at http://www.osakac.ac.jp/labs/shoji/ .
  2. D. B. Judd, D. L. MacAdam, W. S. Stiles, “Spectral distribution of typical daylight as a function of correlated color temperature,” J. Opt. Soc. Am. 54, 1031–1040 (1964).
    [CrossRef]
  3. L. T. Maloney, B. A. Wandell, “Color constancy: a method for recovering surface spectral reflectance,” J. Opt. Soc. Am. A 3, 29–33 (1986).
    [CrossRef] [PubMed]
  4. S. Tominaga, B. A. Wandell, “The standard surface reflectance model and illuminant estimation,” J. Opt. Soc. Am. A 6, 576–584 (1989).
    [CrossRef]
  5. B. V. Funt, M. S. Drew, J. Ho, “Color constancy from mutual reflection,” Int. J. Comput. Vis. 6, 5–24 (1991).
    [CrossRef]
  6. M. D’Zmura, G. Iverson, “Color constancy: I. Basic theory of two-stage linear recovery of spectral descriptions for lights and surfaces,” J. Opt. Soc. Am. A 10, 2148–2165 (1993).
    [CrossRef]
  7. M. D’Zmura, G. Iverson, B. Singer, “Probabilistic color constancy,” in R. D. Luce et al., eds., Geometric Representations of Perceptual Phenomena (Erlbaum, Mahway, N.J., 1995), pp. 187–202.
  8. S. Tominaga, “Multichannel vision system for estimating surface and illumination functions,” J. Opt. Soc. Am. A 13, 2163–2173 (1996).
    [CrossRef]
  9. D. H. Brainard, W. T. Freeman, “Bayesian color constancy,” J. Opt. Soc. Am. A 14, 1393–1411 (1997).
    [CrossRef]
  10. G. D. Finlayson, P. M. Hubel, S. Hordley, “Color by correlation,” in Proceedings of the Fifth Color Imaging Conference (Society for Imaging Science and Technology, Springfield, Va., 1997), pp. 6–11.
  11. G. D. Finlayson, “Color in perspective,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 1034–1038 (1996).
    [CrossRef]
  12. G. Wyszecki, W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae (Wiley, New York, 1982).
  13. M. J. Vrhel, R. Gershon, L. S. Iwan, “Measurement and analysis of object reflectance spectra,” Color Res. Appl. 19, 4–9 (1994).
  14. S. Tominaga, S. Ebisui, B. A. Wandell, “Color temperature estimation of scene illumination,” in Proceedings of the Seventh Color Imaging Conference (Society for Imaging Science and Technology, Springfield, Va., 1999), pp. 42–47.
  15. D. A. Forstyth, “A novel algorithm for color constancy,” Int. J. Comput. Vis. 5, 5–36 (1990).
    [CrossRef]
  16. Y. Yu, P. Debevec, J. Malik, T. Hawkins, “Inverse global illumination: recovering reflectance models of real scenes from photographs,” Special Interest Group on Computer Graphics and Interactive Techniques (SIGGRAPH) 99, 215–224 (1999).
  17. D. H. Brainard, “Color constancy in the nearly natural image. 2. Achromatic loci,” J. Opt. Soc. Am. A 15, 307–325 (1998).
    [CrossRef]

1999 (1)

Y. Yu, P. Debevec, J. Malik, T. Hawkins, “Inverse global illumination: recovering reflectance models of real scenes from photographs,” Special Interest Group on Computer Graphics and Interactive Techniques (SIGGRAPH) 99, 215–224 (1999).

1998 (1)

1997 (1)

1996 (2)

G. D. Finlayson, “Color in perspective,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 1034–1038 (1996).
[CrossRef]

S. Tominaga, “Multichannel vision system for estimating surface and illumination functions,” J. Opt. Soc. Am. A 13, 2163–2173 (1996).
[CrossRef]

1994 (1)

M. J. Vrhel, R. Gershon, L. S. Iwan, “Measurement and analysis of object reflectance spectra,” Color Res. Appl. 19, 4–9 (1994).

1993 (1)

1991 (1)

B. V. Funt, M. S. Drew, J. Ho, “Color constancy from mutual reflection,” Int. J. Comput. Vis. 6, 5–24 (1991).
[CrossRef]

1990 (1)

D. A. Forstyth, “A novel algorithm for color constancy,” Int. J. Comput. Vis. 5, 5–36 (1990).
[CrossRef]

1989 (1)

1986 (1)

1964 (1)

Brainard, D. H.

D’Zmura, M.

M. D’Zmura, G. Iverson, “Color constancy: I. Basic theory of two-stage linear recovery of spectral descriptions for lights and surfaces,” J. Opt. Soc. Am. A 10, 2148–2165 (1993).
[CrossRef]

M. D’Zmura, G. Iverson, B. Singer, “Probabilistic color constancy,” in R. D. Luce et al., eds., Geometric Representations of Perceptual Phenomena (Erlbaum, Mahway, N.J., 1995), pp. 187–202.

Debevec, P.

Y. Yu, P. Debevec, J. Malik, T. Hawkins, “Inverse global illumination: recovering reflectance models of real scenes from photographs,” Special Interest Group on Computer Graphics and Interactive Techniques (SIGGRAPH) 99, 215–224 (1999).

Drew, M. S.

B. V. Funt, M. S. Drew, J. Ho, “Color constancy from mutual reflection,” Int. J. Comput. Vis. 6, 5–24 (1991).
[CrossRef]

Ebisui, S.

S. Tominaga, S. Ebisui, B. A. Wandell, “Color temperature estimation of scene illumination,” in Proceedings of the Seventh Color Imaging Conference (Society for Imaging Science and Technology, Springfield, Va., 1999), pp. 42–47.

Finlayson, G. D.

G. D. Finlayson, “Color in perspective,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 1034–1038 (1996).
[CrossRef]

G. D. Finlayson, P. M. Hubel, S. Hordley, “Color by correlation,” in Proceedings of the Fifth Color Imaging Conference (Society for Imaging Science and Technology, Springfield, Va., 1997), pp. 6–11.

Forstyth, D. A.

D. A. Forstyth, “A novel algorithm for color constancy,” Int. J. Comput. Vis. 5, 5–36 (1990).
[CrossRef]

Freeman, W. T.

Funt, B. V.

B. V. Funt, M. S. Drew, J. Ho, “Color constancy from mutual reflection,” Int. J. Comput. Vis. 6, 5–24 (1991).
[CrossRef]

Gershon, R.

M. J. Vrhel, R. Gershon, L. S. Iwan, “Measurement and analysis of object reflectance spectra,” Color Res. Appl. 19, 4–9 (1994).

Hawkins, T.

Y. Yu, P. Debevec, J. Malik, T. Hawkins, “Inverse global illumination: recovering reflectance models of real scenes from photographs,” Special Interest Group on Computer Graphics and Interactive Techniques (SIGGRAPH) 99, 215–224 (1999).

Ho, J.

B. V. Funt, M. S. Drew, J. Ho, “Color constancy from mutual reflection,” Int. J. Comput. Vis. 6, 5–24 (1991).
[CrossRef]

Hordley, S.

G. D. Finlayson, P. M. Hubel, S. Hordley, “Color by correlation,” in Proceedings of the Fifth Color Imaging Conference (Society for Imaging Science and Technology, Springfield, Va., 1997), pp. 6–11.

Hubel, P. M.

G. D. Finlayson, P. M. Hubel, S. Hordley, “Color by correlation,” in Proceedings of the Fifth Color Imaging Conference (Society for Imaging Science and Technology, Springfield, Va., 1997), pp. 6–11.

Iverson, G.

M. D’Zmura, G. Iverson, “Color constancy: I. Basic theory of two-stage linear recovery of spectral descriptions for lights and surfaces,” J. Opt. Soc. Am. A 10, 2148–2165 (1993).
[CrossRef]

M. D’Zmura, G. Iverson, B. Singer, “Probabilistic color constancy,” in R. D. Luce et al., eds., Geometric Representations of Perceptual Phenomena (Erlbaum, Mahway, N.J., 1995), pp. 187–202.

Iwan, L. S.

M. J. Vrhel, R. Gershon, L. S. Iwan, “Measurement and analysis of object reflectance spectra,” Color Res. Appl. 19, 4–9 (1994).

Judd, D. B.

MacAdam, D. L.

Malik, J.

Y. Yu, P. Debevec, J. Malik, T. Hawkins, “Inverse global illumination: recovering reflectance models of real scenes from photographs,” Special Interest Group on Computer Graphics and Interactive Techniques (SIGGRAPH) 99, 215–224 (1999).

Maloney, L. T.

Singer, B.

M. D’Zmura, G. Iverson, B. Singer, “Probabilistic color constancy,” in R. D. Luce et al., eds., Geometric Representations of Perceptual Phenomena (Erlbaum, Mahway, N.J., 1995), pp. 187–202.

Stiles, W. S.

Tominaga, S.

S. Tominaga, “Multichannel vision system for estimating surface and illumination functions,” J. Opt. Soc. Am. A 13, 2163–2173 (1996).
[CrossRef]

S. Tominaga, B. A. Wandell, “The standard surface reflectance model and illuminant estimation,” J. Opt. Soc. Am. A 6, 576–584 (1989).
[CrossRef]

S. Tominaga, S. Ebisui, B. A. Wandell, “Color temperature estimation of scene illumination,” in Proceedings of the Seventh Color Imaging Conference (Society for Imaging Science and Technology, Springfield, Va., 1999), pp. 42–47.

Vrhel, M. J.

M. J. Vrhel, R. Gershon, L. S. Iwan, “Measurement and analysis of object reflectance spectra,” Color Res. Appl. 19, 4–9 (1994).

Wandell, B. A.

S. Tominaga, B. A. Wandell, “The standard surface reflectance model and illuminant estimation,” J. Opt. Soc. Am. A 6, 576–584 (1989).
[CrossRef]

L. T. Maloney, B. A. Wandell, “Color constancy: a method for recovering surface spectral reflectance,” J. Opt. Soc. Am. A 3, 29–33 (1986).
[CrossRef] [PubMed]

S. Tominaga, S. Ebisui, B. A. Wandell, “Color temperature estimation of scene illumination,” in Proceedings of the Seventh Color Imaging Conference (Society for Imaging Science and Technology, Springfield, Va., 1999), pp. 42–47.

Wyszecki, G.

G. Wyszecki, W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae (Wiley, New York, 1982).

Yu, Y.

Y. Yu, P. Debevec, J. Malik, T. Hawkins, “Inverse global illumination: recovering reflectance models of real scenes from photographs,” Special Interest Group on Computer Graphics and Interactive Techniques (SIGGRAPH) 99, 215–224 (1999).

Color Res. Appl. (1)

M. J. Vrhel, R. Gershon, L. S. Iwan, “Measurement and analysis of object reflectance spectra,” Color Res. Appl. 19, 4–9 (1994).

IEEE Trans. Pattern Anal. Mach. Intell. (1)

G. D. Finlayson, “Color in perspective,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 1034–1038 (1996).
[CrossRef]

Int. J. Comput. Vis. (2)

B. V. Funt, M. S. Drew, J. Ho, “Color constancy from mutual reflection,” Int. J. Comput. Vis. 6, 5–24 (1991).
[CrossRef]

D. A. Forstyth, “A novel algorithm for color constancy,” Int. J. Comput. Vis. 5, 5–36 (1990).
[CrossRef]

J. Opt. Soc. Am. (1)

J. Opt. Soc. Am. A (6)

Special Interest Group on Computer Graphics and Interactive Techniques (SIGGRAPH) (1)

Y. Yu, P. Debevec, J. Malik, T. Hawkins, “Inverse global illumination: recovering reflectance models of real scenes from photographs,” Special Interest Group on Computer Graphics and Interactive Techniques (SIGGRAPH) 99, 215–224 (1999).

Other (5)

S. Tominaga, S. Ebisui, B. A. Wandell, “Color temperature estimation of scene illumination,” in Proceedings of the Seventh Color Imaging Conference (Society for Imaging Science and Technology, Springfield, Va., 1999), pp. 42–47.

M. D’Zmura, G. Iverson, B. Singer, “Probabilistic color constancy,” in R. D. Luce et al., eds., Geometric Representations of Perceptual Phenomena (Erlbaum, Mahway, N.J., 1995), pp. 187–202.

G. D. Finlayson, P. M. Hubel, S. Hordley, “Color by correlation,” in Proceedings of the Fifth Color Imaging Conference (Society for Imaging Science and Technology, Springfield, Va., 1997), pp. 6–11.

G. Wyszecki, W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae (Wiley, New York, 1982).

The image data used in this paper will be available at http://www.osakac.ac.jp/labs/shoji/ .

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Figures (16)

Fig. 1
Fig. 1

Spectral-sensitivity functions of a camera: (a) tungsten mode, (b) daylight mode.

Fig. 2
Fig. 2

Spectral power distributions of black body radiators.

Fig. 3
Fig. 3

Illuminant gamuts for blackbody radiators in the (r, b) chromaticity plane.

Fig. 4
Fig. 4

Illuminant gamuts for blackbody radiators in the (R, B) sensor plane.

Fig. 5
Fig. 5

Correlation coefficients between adjacent gamuts for the sensor sensitivity functions in Figs. 1(a) and 1(b).

Fig. 6
Fig. 6

Example of a natural scene.

Fig. 7
Fig. 7

Correlation function for the image shown in Fig. 6. The solid and the dashed curves represent, respectively, the proposed sensor correlation and the chromaticity correlation.

Fig. 8
Fig. 8

Set of 14 images of indoor scenes under an incandescent lamp.

Fig. 9
Fig. 9

Set of correlation functions for the indoor images. The solid and the dashed curves represent, respectively, the sensor correlation and the chromaticity correlation.

Fig. 10
Fig. 10

Set of images of outdoor scenes.

Fig. 11
Fig. 11

Set of correlation functions for the outdoor images. The solid and the dashed curves represent, respectively, the sensor correlation and the chromaticity correlation.

Fig. 12
Fig. 12

Color temperature errors as a range of spectral power distribution. The solid curves represent the estimated spectral distribution, and the dashed curves represent the average full spectra of the measured illuminants.

Fig. 13
Fig. 13

Image data points grouped according to intensity for the natural scene in Fig. 6. The five types of labeled a,b ,…, e represent five intensity ranges, corresponding to the percentiles 0–20, 20–40, and so forth.

Fig. 14
Fig. 14

Binary image showing the top 20% of pixel intensities in the natural scene.

Fig. 15
Fig. 15

Set of correlation functions calculated separately for each of the intensity ranges.

Fig. 16
Fig. 16

Graphical example illustrating the difference between sensor values and chromaticity coordinates.

Equations (6)

Equations on this page are rendered with MathJax. Learn more.

M(λ)=c1λ-5[exp(c2/λT)-1]-1,
RGB=400700S(λ)M(λ)r(λ)g(λ)b(λ)dλ,
r=R/(R+G+B),b=B/(R+G+B).
Ii=(Ri2+Gi2+Bi2)1/2,
(R, G, B)=(R/Imax, G/Imax, B/Imax).
Cor=i,jxijyij/i,jxij2i,jyij21/2,

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