Abstract

Existing color constancy methods cannot handle both uniformly colored surfaces and highly textured surfaces in a single integrated framework. Statistics-based methods require many surface colors and become error prone when there are only a few surface colors. In contrast, dichromatic-based methods can successfully handle uniformly colored surfaces but cannot be applied to highly textured surfaces, since they require precise color segmentation. We present a single integrated method to estimate illumination chromaticity from single-colored and multicolored surfaces. Unlike existing dichromatic-based methods, the proposed method requires only rough highlight regions without segmenting the colors inside them. We show that, by analyzing highlights, a direct correlation between illumination chromaticity and image chromaticity can be obtained. This correlation is clearly described in “inverse-intensity chromaticity space,” a novel two-dimensional space that we introduce. In addition, when Hough transform and histogram analysis is utilized in this space, illumination chromaticity can be estimated robustly, even for a highly textured surface.

© 2004 Optical Society of America

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References

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  1. G. F. Finlayson, G. Schaefer, “Solving for colour constancy using a constrained dichromatic reflection model,” Int. J. Comput. Vision 42, 127–144 (2001).
    [CrossRef]
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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
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    [CrossRef]

2002 (1)

S. Tominaga, B. A. Wandell, “Natural scene-illuminant estimation using the sensor correlation,” Proc. IEEE 90, 42–56 (2002).
[CrossRef]

2001 (6)

G. D. Finlayson, S. D. Hordley, P. M. Hubel, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1209–1221 (2001).
[CrossRef]

G. F. Finlayson, G. Schaefer, “Solving for colour constancy using a constrained dichromatic reflection model,” Int. J. Comput. Vision 42, 127–144 (2001).
[CrossRef]

J. M. Geusebroek, R. Boomgaard, S. Smeulders, H. Geert, “Color invariance,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1338–1350 (2001).
[CrossRef]

S. Tominaga, S. Ebisui, B. A. Wandell, “Scene illuminant classification: brighter is better,” J. Opt. Soc. Am. A 18, 55–64 (2001).
[CrossRef]

G. F. Finlayson, S. D. Hordley, “Color constancy at a pixel,” J. Opt. Soc. Am. A 18, 253–264 (2001).
[CrossRef]

T. M. Lehmann, C. Palm, “Color line search for illuminant estimation in real-world scene,” J. Opt. Soc. Am. A 18, 2679–2691 (2001).
[CrossRef]

2000 (1)

1999 (1)

G. Sapiro, “Color and Illumination Voting,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 1210–1215 (1999).
[CrossRef]

1997 (1)

1996 (2)

G. D. Finlayson, “Color in perspective,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 1034–1036 (1996).
[CrossRef]

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

1994 (1)

G. D. Finlayson, B. V. Funt, “Color constancy using shadows,” Perception 23, 89–90 (1994).

1991 (1)

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

1990 (2)

G. J. Klinker, S. A. Shafer, T. Kanade, “The measurement of highlights in color images,” Int. J. Comput. Vis. 2, 7–32 (1990).
[CrossRef]

H. C. Lee, E. J. Breneman, C. P. Schulte, “Modeling light reflection for computer color vision,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 402–409 (1990).
[CrossRef]

1989 (2)

1986 (2)

H. C. Lee, “Method for computing the scene-illuminant from specular highlights,” J. Opt. Soc. Am. A 3, 1694–1699 (1986).
[CrossRef] [PubMed]

M. D’Zmura, P. Lennie, “Mechanism of color constancy,” J. Opt. Soc. Am. A 3, 1162–1672 (1986).

1985 (1)

S. Shafer, “Using color to separate reflection components,” Color Res. Appl. 10, 210–218 (1985).
[CrossRef]

1967 (1)

Andersen, H. J.

Boomgaard, R.

J. M. Geusebroek, R. Boomgaard, S. Smeulders, H. Geert, “Color invariance,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1338–1350 (2001).
[CrossRef]

J. M. Geusebroek, R. Boomgaard, S. Smeulders, T. Gevers, “A physical basis for color constancy,” in Proceedings ofthe First European Conference on Colour in Graphics, Image and Vision (Society for Imaging Science and Technology, Springfield, Va., 2002), pp. 3–6.

Brainard, D. H.

Breneman, E. J.

H. C. Lee, E. J. Breneman, C. P. Schulte, “Modeling light reflection for computer color vision,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 402–409 (1990).
[CrossRef]

D’Zmura, M.

M. D’Zmura, P. Lennie, “Mechanism of color constancy,” J. Opt. Soc. Am. A 3, 1162–1672 (1986).

Drew, M.

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

Ebisui, S.

Finlayson, G. D.

G. D. Finlayson, S. D. Hordley, P. M. Hubel, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1209–1221 (2001).
[CrossRef]

G. D. Finlayson, “Color in perspective,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 1034–1036 (1996).
[CrossRef]

G. D. Finlayson, B. V. Funt, “Color constancy using shadows,” Perception 23, 89–90 (1994).

G. D. Finlayson, G. Schaefer, “Convex and non-convex illumination constraints for dichromatic color constancy,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, New York, 2001), pp. 598–605.

Finlayson, G. F.

G. F. Finlayson, G. Schaefer, “Solving for colour constancy using a constrained dichromatic reflection model,” Int. J. Comput. Vision 42, 127–144 (2001).
[CrossRef]

G. F. Finlayson, S. D. Hordley, “Color constancy at a pixel,” J. Opt. Soc. Am. A 18, 253–264 (2001).
[CrossRef]

Freeman, W. T.

Funt, B. V.

G. D. Finlayson, B. V. Funt, “Color constancy using shadows,” Perception 23, 89–90 (1994).

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

Geert, H.

J. M. Geusebroek, R. Boomgaard, S. Smeulders, H. Geert, “Color invariance,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1338–1350 (2001).
[CrossRef]

Geusebroek, J. M.

J. M. Geusebroek, R. Boomgaard, S. Smeulders, H. Geert, “Color invariance,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1338–1350 (2001).
[CrossRef]

J. M. Geusebroek, R. Boomgaard, S. Smeulders, T. Gevers, “A physical basis for color constancy,” in Proceedings ofthe First European Conference on Colour in Graphics, Image and Vision (Society for Imaging Science and Technology, Springfield, Va., 2002), pp. 3–6.

Gevers, T.

J. M. Geusebroek, R. Boomgaard, S. Smeulders, T. Gevers, “A physical basis for color constancy,” in Proceedings ofthe First European Conference on Colour in Graphics, Image and Vision (Society for Imaging Science and Technology, Springfield, Va., 2002), pp. 3–6.

Granum, E.

Healey, G.

Hebert, M.

C. Rosenberg, M. Hebert, S. Thrun, “Color constancy using KL-divergence,” in Proceedings of the IEEE International Conference on Computer Vision (Institute of Electrical and Electronics Engineers, New York, 2001), pp. 239–247.

Ho, J.

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

Hordley, S. D.

G. D. Finlayson, S. D. Hordley, P. M. Hubel, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1209–1221 (2001).
[CrossRef]

G. F. Finlayson, S. D. Hordley, “Color constancy at a pixel,” J. Opt. Soc. Am. A 18, 253–264 (2001).
[CrossRef]

Hubel, P. M.

G. D. Finlayson, S. D. Hordley, P. M. Hubel, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1209–1221 (2001).
[CrossRef]

Kanade, T.

G. J. Klinker, S. A. Shafer, T. Kanade, “The measurement of highlights in color images,” Int. J. Comput. Vis. 2, 7–32 (1990).
[CrossRef]

Klinker, G. J.

G. J. Klinker, S. A. Shafer, T. Kanade, “The measurement of highlights in color images,” Int. J. Comput. Vis. 2, 7–32 (1990).
[CrossRef]

G. J. Klinker, “A physical approach to color image understanding,” PhD. thesis (Carnegie Mellon University, Pittsburgh, Pa., 1988).

Lambert, J. H.

J. H. Lambert, Photometria sive de mensura de gratibus luminis, colorum et umbrae (Eberhard Klett, Augsberg, Germany, 1760).

Lee, H. C.

H. C. Lee, E. J. Breneman, C. P. Schulte, “Modeling light reflection for computer color vision,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 402–409 (1990).
[CrossRef]

H. C. Lee, “Method for computing the scene-illuminant from specular highlights,” J. Opt. Soc. Am. A 3, 1694–1699 (1986).
[CrossRef] [PubMed]

H. C. Lee, “Illuminant color from shading,” in Physics-Based Vision Principle and Practice: Color (Jones and Bartlett, Boston, Mass., 1992), pp. 340–347.

Lehmann, T. M.

Lennie, P.

M. D’Zmura, P. Lennie, “Mechanism of color constancy,” J. Opt. Soc. Am. A 3, 1162–1672 (1986).

Palm, C.

Rosenberg, C.

C. Rosenberg, M. Hebert, S. Thrun, “Color constancy using KL-divergence,” in Proceedings of the IEEE International Conference on Computer Vision (Institute of Electrical and Electronics Engineers, New York, 2001), pp. 239–247.

Sapiro, G.

G. Sapiro, “Color and Illumination Voting,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 1210–1215 (1999).
[CrossRef]

Schaefer, G.

G. F. Finlayson, G. Schaefer, “Solving for colour constancy using a constrained dichromatic reflection model,” Int. J. Comput. Vision 42, 127–144 (2001).
[CrossRef]

G. D. Finlayson, G. Schaefer, “Convex and non-convex illumination constraints for dichromatic color constancy,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, New York, 2001), pp. 598–605.

Schulte, C. P.

H. C. Lee, E. J. Breneman, C. P. Schulte, “Modeling light reflection for computer color vision,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 402–409 (1990).
[CrossRef]

Shafer, S.

S. Shafer, “Using color to separate reflection components,” Color Res. Appl. 10, 210–218 (1985).
[CrossRef]

Shafer, S. A.

G. J. Klinker, S. A. Shafer, T. Kanade, “The measurement of highlights in color images,” Int. J. Comput. Vis. 2, 7–32 (1990).
[CrossRef]

Smeulders, S.

J. M. Geusebroek, R. Boomgaard, S. Smeulders, H. Geert, “Color invariance,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1338–1350 (2001).
[CrossRef]

J. M. Geusebroek, R. Boomgaard, S. Smeulders, T. Gevers, “A physical basis for color constancy,” in Proceedings ofthe First European Conference on Colour in Graphics, Image and Vision (Society for Imaging Science and Technology, Springfield, Va., 2002), pp. 3–6.

Sparrow, E. M.

Thrun, S.

C. Rosenberg, M. Hebert, S. Thrun, “Color constancy using KL-divergence,” in Proceedings of the IEEE International Conference on Computer Vision (Institute of Electrical and Electronics Engineers, New York, 2001), pp. 239–247.

Tominaga, S.

Torrance, K. E.

Wandell, B. A.

Color Res. Appl. (1)

S. Shafer, “Using color to separate reflection components,” Color Res. Appl. 10, 210–218 (1985).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell. (5)

J. M. Geusebroek, R. Boomgaard, S. Smeulders, H. Geert, “Color invariance,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1338–1350 (2001).
[CrossRef]

H. C. Lee, E. J. Breneman, C. P. Schulte, “Modeling light reflection for computer color vision,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 402–409 (1990).
[CrossRef]

G. D. Finlayson, “Color in perspective,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 1034–1036 (1996).
[CrossRef]

G. D. Finlayson, S. D. Hordley, P. M. Hubel, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1209–1221 (2001).
[CrossRef]

G. Sapiro, “Color and Illumination Voting,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 1210–1215 (1999).
[CrossRef]

Int. J. Comput. Vis. (2)

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

G. J. Klinker, S. A. Shafer, T. Kanade, “The measurement of highlights in color images,” Int. J. Comput. Vis. 2, 7–32 (1990).
[CrossRef]

Int. J. Comput. Vision (1)

G. F. Finlayson, G. Schaefer, “Solving for colour constancy using a constrained dichromatic reflection model,” Int. J. Comput. Vision 42, 127–144 (2001).
[CrossRef]

J. Opt. Soc. Am. (1)

J. Opt. Soc. Am. A (10)

Perception (1)

G. D. Finlayson, B. V. Funt, “Color constancy using shadows,” Perception 23, 89–90 (1994).

Proc. IEEE (1)

S. Tominaga, B. A. Wandell, “Natural scene-illuminant estimation using the sensor correlation,” Proc. IEEE 90, 42–56 (2002).
[CrossRef]

Other (6)

C. Rosenberg, M. Hebert, S. Thrun, “Color constancy using KL-divergence,” in Proceedings of the IEEE International Conference on Computer Vision (Institute of Electrical and Electronics Engineers, New York, 2001), pp. 239–247.

H. C. Lee, “Illuminant color from shading,” in Physics-Based Vision Principle and Practice: Color (Jones and Bartlett, Boston, Mass., 1992), pp. 340–347.

G. J. Klinker, “A physical approach to color image understanding,” PhD. thesis (Carnegie Mellon University, Pittsburgh, Pa., 1988).

J. M. Geusebroek, R. Boomgaard, S. Smeulders, T. Gevers, “A physical basis for color constancy,” in Proceedings ofthe First European Conference on Colour in Graphics, Image and Vision (Society for Imaging Science and Technology, Springfield, Va., 2002), pp. 3–6.

J. H. Lambert, Photometria sive de mensura de gratibus luminis, colorum et umbrae (Eberhard Klett, Augsberg, Germany, 1760).

G. D. Finlayson, G. Schaefer, “Convex and non-convex illumination constraints for dichromatic color constancy,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, New York, 2001), pp. 598–605.

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Figures (16)

Fig. 1
Fig. 1

(a) Synthetic image with a single surface color; (b) projection of the diffuse and specular pixels into the chromaticity-intensity space, with c representing the green channel.

Fig. 2
Fig. 2

(a) Sketch of specular points of a uniformly colored surface in inverse-intensity chromaticity space, (b) sketch of specular points of two different surface colors.

Fig. 3
Fig. 3

(a) Diffuse and specular points of a synthetic image [Fig. 1(a)] in inverse-intensity chromaticity space, with c representing the green channel; (b) cluster of specular points that head for the illumination-chromaticity value in the y axis.

Fig. 4
Fig. 4

(a) Synthetic image with multiple surface colors; (b) specular points in inverse-intensity chromaticity space, with c representing the green channel.

Fig. 5
Fig. 5

(a) Projection of points in Fig. 3(b) into Hough space, (b) sketch of intersected lines in Hough space.

Fig. 6
Fig. 6

Intersection-counting distribution of green channel. Estimated illumination chromaticity: Γr=0.535, Γb=0.303, Γb=0.162; ground-truth values: Γr=0.536, Γb=0.304, Γb=0.160.

Fig. 7
Fig. 7

Distribution of specular and diffuse pixels in inverse-intensity chromaticity space when md is constant.

Fig. 8
Fig. 8

Distribution of specular and diffuse pixels in inverse-intensity chromaticity space when md varies.

Fig. 9
Fig. 9

(a) Real input image with a single surface color, (b) projection of the red channel of the specular pixels into inverse-intensity chromaticity space, (c) projection of the green channel of the specular pixels into inverse-intensity chromaticity space, (d) projection of the blue channel of the specular pixels into inverse-intensity chromaticity space.

Fig. 10
Fig. 10

(a) Intersection-counting distribution for the red channel of illumination chromaticity for the image in Fig. 9, (b) intersection-counting distribution for the green channel, (c) intersection-counting distribution for the blue channel.

Fig. 11
Fig. 11

(a) Real input image with multiple surface colors, (b) projection of the red channel of the specular pixels into inverse-intensity chromaticity space, (c) projection of the green channel of the specular pixels into inverse-intensity chromaticity space, (d) projection of the blue channel of the specular pixels into inverse-intensity chromaticity space.

Fig. 12
Fig. 12

(a) Intersection-counting distribution for the red channel of illumination chromaticity for the image in Fig. 11, (b) intersection-counting distribution for the green channel, (c) intersection-counting distribution for the blue channel.

Fig. 13
Fig. 13

(a) Real input image of complex textured surface, (b) projection of the red channel of the specular pixels into inverse-intensity chromaticity space, (c) projection of the green channel of the specular pixels into inverse-intensity chromaticity space, (d) projection of the green channel of the specular pixels into inverse-intensity chromaticity space.

Fig. 14
Fig. 14

(a) Intersection-counting distribution for the red channel of illumination chromaticity for the image in Fig. 13, (b) intersection-counting distribution for the green channel, (c) intersection-counting distribution for the blue channel.

Fig. 15
Fig. 15

(a) Real input image of a scene with multiple objects; (b) result of projecting the specular pixels into inverse-intensity chromaticity space, with c representing the red channel; (c) result of projecting the specular pixels, with c representing the green channel; (d) result of projecting the specular pixels, with c representing the blue channel.

Fig. 16
Fig. 16

(a) Intersection-counting distribution for the red channel of illumination chromaticity for the image in Fig. 13, (b) intersection-counting distribution for the green channel, (c) intersection-counting distribution for the blue channel.

Tables (2)

Tables Icon

Table 1 Performance of the Estimation Method with Regard to the Image Chromaticity of the White Reference

Tables Icon

Table 2 Estimation Results Using the Database of Lehmann and Palm

Equations (19)

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

I(λ, x¯)=wd(x¯)Sd(λ, x¯)E(λ, x¯)+ws(x¯)Ss(λ, x¯)E(λ, x¯),
I(λ, x¯)=wd(x¯)Sd(λ, x¯)E(λ, x¯)+w˜s(x¯)E(λ, x¯),
Ic(x)=wd(x)ΩSd(λ, x)E(λ)qc(λ)dλ+w˜s(x)ΩE(λ)qc(λ)dλ,
Ic(x)=wd(x)Bc(x)+w˜s(x)Gc,
σc(x)=Ic(x)Ii(x),
Λc(x)=Bc(x)Bi(x).
Γc=GcGi.
Ic(x)=md(x)Λc(x)+ms(x)Γc,
md(x)=wd(x)Bi(x),
ms(x)=w˜d(x)Gi.
σc=mdΛc+msΓcmdΛi+msΓi.
ms=md(Λc-σc)σc-Γc.
Ic=md(Λc-Γc)σcσc-Γc.
Icσc=pσc-Γc.
σc=p1Ii+Γc.
w˜s=FG1cos θrexp-α22ϕ2,
h=msAmdA(mdA+msA)[1+(mdA)2(Λc-Γc)2]1/2,
dj=msjmdj+msj(Λc-Γc),
I˜=Ir+Ig+Ib3>TaI˜max,S˜=1-min(Ir, Ig, Ib)I˜<TbS˜max,

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