Abstract

We investigate and propose a method for assessment of the illumination condition covering two light sources. The method may be of some support for color vision and multispectral analysis methods that rely on a specific illumination condition. It is constrained to classifying the illumination condition for dielectric objects illuminated by two light sources. The reflected light is modeled by the dichromatic reflection model, which describes the light as the sum of its body reflections and surface reflections. Further, reflected light from an object illuminated by two light sources may give from one to four primary reflections depending on the condition, and it may be expressed as an additive mixture of these reflections. An additive mixture of two reflections expressed in chromaticities is limited to falling within the area enclosed by the chromaticities of the primary reflections of the light sources. So after finding the set of primary chromaticities enclosing the pixel points’ chromaticities, it is possible for one to assess the current illumination condition. Since the method operates on pixel points globally, it is independent of illumination geometry and hence may be used on irregular objects. Two experiments are performed. One uses regular objects in a well-controlled laboratory environment and demonstrates that the pixel-point distribution is as expected. The second experiment demonstrates the method’s potential use in support of spectroscopic analysis of vegetation through assessing the illumination condition of barley plants in an outdoor illumination condition.

© 2000 Optical Society of America

Full Article  |  PDF Article

References

  • View by:
  • |
  • |
  • |

  1. S. A. Shafer, “Using color to separate reflection components,” Color Res. Appl. 10, 210–218 (1985).
    [CrossRef]
  2. G. J. Klinker, S. A. Shafer, T. Kanada, “A physical approach to color image understanding,” Int. J. Comput. Vision 1, 7–38 (1990).
    [CrossRef]
  3. H.-C. Lee, “Method for computing the scene-illuminant chromaticity from specular highlights,” J. Opt. Soc. Am. A 3, 1694–1699 (1986).
    [CrossRef] [PubMed]
  4. C. L. Novak, S. A. Shafer, “Method for estimating scene parameters from color histograms,” J. Opt. Soc. Am. A 11, 3020–3036 (1994).
    [CrossRef]
  5. S. Tominaga, B. A. Wandell, “Standard surface-reflectance model and illuminant estimation,” J. Opt. Soc. Am. A 6, 576–584 (1989).
    [CrossRef]
  6. S. Tominaga, “Dichromatic reflection models for rendering object surfaces,” J. Imaging Sci. Technol. 40, 549–555 (1996).
  7. K. Torrance, E. Sparrow, “Theory for off-specular reflection from roughened surfaces,” J. Opt. Soc. Am. 57, 1105–1114 (1967).
    [CrossRef]
  8. B. A. Maxwell, S. A. Shafer, “Physics-based segmentation of complex objects using multiple hypotheses of image formation,” Comput. Vision Image Underst. 65, 265–295 (1997).
    [CrossRef]
  9. H.-C. Lee, E. J. Breneman, C. P. Schulte, “Modeling light reflection for color computer vision,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 402–409 (1990).
    [CrossRef]
  10. S. Tominaga, “Dichromatic reflection models for a variety of materials,” Color Res. Appl. 19, 277–285 (1994).
    [CrossRef]
  11. F. Pla, F. Ferri, M. Vicens, “Colour segmentation based on a light reflection model to locate citrus for robotic harvesting,” Comput. Electron. Agr. 9, 53–70 (1993).
    [CrossRef]
  12. H.-C. Lee, “Illuminant color from shading,” in Perceiving, Measuring, and Using Color, M. H. Brill, ed., Proc. SPIE1250, 236–244 (1990).
    [CrossRef]
  13. “Method of measuring and specifying colour rendering properties of light sources,” [Commission Internationale de L’Eclairage (CIE), Vienna, 1995].
  14. “Colorimetry,” , 2nd ed. (CIE, Vienna, 1986).
  15. D. B. Judd, D. L. MacAdam, G. W. Wyszecki, “Spectral distribution of typical daylight as a function of correlated color temperature,” J. Opt. Soc. Am. 54, 1031–1040 (1964).
    [CrossRef]
  16. S. J. Maas, J. R. Dunlap, “Reflectance, transmittance, and absorptance of light by normal, etiolated, and albino corn leaves,” Agron. J. 81, 105–110 (1989).
    [CrossRef]
  17. C. A. Shull, “A spectrophotometric study of plant reflection of light from leaf surfaces,” Bot. Gaz. 87, 583–607 (1929).
    [CrossRef]
  18. W. D. Billings, R. J. Morris, “Reflection of visible and infrared radiation from leaves of different ecological groups,” Am. J. Bot. 38, 327–331 (1951).
    [CrossRef]
  19. J. T. Wooley, “Reflectance and transmittance of light by leaves,” Plant Physiol. 47, 656–662 (1971).
    [CrossRef]
  20. E. A. Walter-Shea, J. M. Norman, “Leaf optical properties,” in Photon–Vegetation Interactions, R. B. Mynemi, J. Ross, eds. (Springer-Verlag, Berlin, 1991), pp. 230–250.

1997 (1)

B. A. Maxwell, S. A. Shafer, “Physics-based segmentation of complex objects using multiple hypotheses of image formation,” Comput. Vision Image Underst. 65, 265–295 (1997).
[CrossRef]

1996 (1)

S. Tominaga, “Dichromatic reflection models for rendering object surfaces,” J. Imaging Sci. Technol. 40, 549–555 (1996).

1994 (2)

C. L. Novak, S. A. Shafer, “Method for estimating scene parameters from color histograms,” J. Opt. Soc. Am. A 11, 3020–3036 (1994).
[CrossRef]

S. Tominaga, “Dichromatic reflection models for a variety of materials,” Color Res. Appl. 19, 277–285 (1994).
[CrossRef]

1993 (1)

F. Pla, F. Ferri, M. Vicens, “Colour segmentation based on a light reflection model to locate citrus for robotic harvesting,” Comput. Electron. Agr. 9, 53–70 (1993).
[CrossRef]

1990 (2)

G. J. Klinker, S. A. Shafer, T. Kanada, “A physical approach to color image understanding,” Int. J. Comput. Vision 1, 7–38 (1990).
[CrossRef]

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

1989 (2)

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

S. J. Maas, J. R. Dunlap, “Reflectance, transmittance, and absorptance of light by normal, etiolated, and albino corn leaves,” Agron. J. 81, 105–110 (1989).
[CrossRef]

1986 (1)

1985 (1)

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

1971 (1)

J. T. Wooley, “Reflectance and transmittance of light by leaves,” Plant Physiol. 47, 656–662 (1971).
[CrossRef]

1967 (1)

1964 (1)

1951 (1)

W. D. Billings, R. J. Morris, “Reflection of visible and infrared radiation from leaves of different ecological groups,” Am. J. Bot. 38, 327–331 (1951).
[CrossRef]

1929 (1)

C. A. Shull, “A spectrophotometric study of plant reflection of light from leaf surfaces,” Bot. Gaz. 87, 583–607 (1929).
[CrossRef]

Billings, W. D.

W. D. Billings, R. J. Morris, “Reflection of visible and infrared radiation from leaves of different ecological groups,” Am. J. Bot. 38, 327–331 (1951).
[CrossRef]

Breneman, E. J.

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

Dunlap, J. R.

S. J. Maas, J. R. Dunlap, “Reflectance, transmittance, and absorptance of light by normal, etiolated, and albino corn leaves,” Agron. J. 81, 105–110 (1989).
[CrossRef]

Ferri, F.

F. Pla, F. Ferri, M. Vicens, “Colour segmentation based on a light reflection model to locate citrus for robotic harvesting,” Comput. Electron. Agr. 9, 53–70 (1993).
[CrossRef]

Judd, D. B.

Kanada, T.

G. J. Klinker, S. A. Shafer, T. Kanada, “A physical approach to color image understanding,” Int. J. Comput. Vision 1, 7–38 (1990).
[CrossRef]

Klinker, G. J.

G. J. Klinker, S. A. Shafer, T. Kanada, “A physical approach to color image understanding,” Int. J. Comput. Vision 1, 7–38 (1990).
[CrossRef]

Lee, H.-C.

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

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

H.-C. Lee, “Illuminant color from shading,” in Perceiving, Measuring, and Using Color, M. H. Brill, ed., Proc. SPIE1250, 236–244 (1990).
[CrossRef]

Maas, S. J.

S. J. Maas, J. R. Dunlap, “Reflectance, transmittance, and absorptance of light by normal, etiolated, and albino corn leaves,” Agron. J. 81, 105–110 (1989).
[CrossRef]

MacAdam, D. L.

Maxwell, B. A.

B. A. Maxwell, S. A. Shafer, “Physics-based segmentation of complex objects using multiple hypotheses of image formation,” Comput. Vision Image Underst. 65, 265–295 (1997).
[CrossRef]

Morris, R. J.

W. D. Billings, R. J. Morris, “Reflection of visible and infrared radiation from leaves of different ecological groups,” Am. J. Bot. 38, 327–331 (1951).
[CrossRef]

Norman, J. M.

E. A. Walter-Shea, J. M. Norman, “Leaf optical properties,” in Photon–Vegetation Interactions, R. B. Mynemi, J. Ross, eds. (Springer-Verlag, Berlin, 1991), pp. 230–250.

Novak, C. L.

Pla, F.

F. Pla, F. Ferri, M. Vicens, “Colour segmentation based on a light reflection model to locate citrus for robotic harvesting,” Comput. Electron. Agr. 9, 53–70 (1993).
[CrossRef]

Schulte, C. P.

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

Shafer, S. A.

B. A. Maxwell, S. A. Shafer, “Physics-based segmentation of complex objects using multiple hypotheses of image formation,” Comput. Vision Image Underst. 65, 265–295 (1997).
[CrossRef]

C. L. Novak, S. A. Shafer, “Method for estimating scene parameters from color histograms,” J. Opt. Soc. Am. A 11, 3020–3036 (1994).
[CrossRef]

G. J. Klinker, S. A. Shafer, T. Kanada, “A physical approach to color image understanding,” Int. J. Comput. Vision 1, 7–38 (1990).
[CrossRef]

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

Shull, C. A.

C. A. Shull, “A spectrophotometric study of plant reflection of light from leaf surfaces,” Bot. Gaz. 87, 583–607 (1929).
[CrossRef]

Sparrow, E.

Tominaga, S.

S. Tominaga, “Dichromatic reflection models for rendering object surfaces,” J. Imaging Sci. Technol. 40, 549–555 (1996).

S. Tominaga, “Dichromatic reflection models for a variety of materials,” Color Res. Appl. 19, 277–285 (1994).
[CrossRef]

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

Torrance, K.

Vicens, M.

F. Pla, F. Ferri, M. Vicens, “Colour segmentation based on a light reflection model to locate citrus for robotic harvesting,” Comput. Electron. Agr. 9, 53–70 (1993).
[CrossRef]

Walter-Shea, E. A.

E. A. Walter-Shea, J. M. Norman, “Leaf optical properties,” in Photon–Vegetation Interactions, R. B. Mynemi, J. Ross, eds. (Springer-Verlag, Berlin, 1991), pp. 230–250.

Wandell, B. A.

Wooley, J. T.

J. T. Wooley, “Reflectance and transmittance of light by leaves,” Plant Physiol. 47, 656–662 (1971).
[CrossRef]

Wyszecki, G. W.

Agron. J. (1)

S. J. Maas, J. R. Dunlap, “Reflectance, transmittance, and absorptance of light by normal, etiolated, and albino corn leaves,” Agron. J. 81, 105–110 (1989).
[CrossRef]

Am. J. Bot. (1)

W. D. Billings, R. J. Morris, “Reflection of visible and infrared radiation from leaves of different ecological groups,” Am. J. Bot. 38, 327–331 (1951).
[CrossRef]

Bot. Gaz. (1)

C. A. Shull, “A spectrophotometric study of plant reflection of light from leaf surfaces,” Bot. Gaz. 87, 583–607 (1929).
[CrossRef]

Color Res. Appl. (2)

S. Tominaga, “Dichromatic reflection models for a variety of materials,” Color Res. Appl. 19, 277–285 (1994).
[CrossRef]

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

Comput. Electron. Agr. (1)

F. Pla, F. Ferri, M. Vicens, “Colour segmentation based on a light reflection model to locate citrus for robotic harvesting,” Comput. Electron. Agr. 9, 53–70 (1993).
[CrossRef]

Comput. Vision Image Underst. (1)

B. A. Maxwell, S. A. Shafer, “Physics-based segmentation of complex objects using multiple hypotheses of image formation,” Comput. Vision Image Underst. 65, 265–295 (1997).
[CrossRef]

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

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

Int. J. Comput. Vision (1)

G. J. Klinker, S. A. Shafer, T. Kanada, “A physical approach to color image understanding,” Int. J. Comput. Vision 1, 7–38 (1990).
[CrossRef]

J. Imaging Sci. Technol. (1)

S. Tominaga, “Dichromatic reflection models for rendering object surfaces,” J. Imaging Sci. Technol. 40, 549–555 (1996).

J. Opt. Soc. Am. (2)

J. Opt. Soc. Am. A (3)

Plant Physiol. (1)

J. T. Wooley, “Reflectance and transmittance of light by leaves,” Plant Physiol. 47, 656–662 (1971).
[CrossRef]

Other (4)

E. A. Walter-Shea, J. M. Norman, “Leaf optical properties,” in Photon–Vegetation Interactions, R. B. Mynemi, J. Ross, eds. (Springer-Verlag, Berlin, 1991), pp. 230–250.

H.-C. Lee, “Illuminant color from shading,” in Perceiving, Measuring, and Using Color, M. H. Brill, ed., Proc. SPIE1250, 236–244 (1990).
[CrossRef]

“Method of measuring and specifying colour rendering properties of light sources,” [Commission Internationale de L’Eclairage (CIE), Vienna, 1995].

“Colorimetry,” , 2nd ed. (CIE, Vienna, 1986).

Cited By

OSA participates in CrossRef's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (11)

Fig. 1
Fig. 1

Perspective projection of a sphere with reflections: M1, due to illuminant E1, and M2, due to illuminant E2. (a) Reflection due to only one of the illuminants E1 or E2; (b) reflections due to each illumination source, E1 and E2, or E1,E2, a mixture of both; (c) same as (b) but with illumination source E1 as an ambient light source.

Fig. 2
Fig. 2

Possible pixel-point locations for a dielectric object illuminated by light sources E1 and E2 having primary chromaticities b(E1), b(E2), s(E1), and s(E2). (a) Primary chromaticities form a convex tetragon, (b) only one light source produces surface reflection. Possible pixel-point locations are limited to areas within the interior line segments.

Fig. 3
Fig. 3

Alternative positions in the chromaticity plane of the dichromatic plane due to varying CCT’s of the illuminant E1, , En.

Fig. 4
Fig. 4

Finding the location in the chromaticity plane of pixel-point distribution under the daylight heuristic. The location is found by spanning a wedge with its apex at the chromaticity of the surface reflection from the sunlight s(E1) and in the direction of the first leg after the body reflection at the corresponding CCT b(E1) and in the direction of the second leg at the CCT of the skylight b(E2). The dashed line illustrates the variable second leg in the scanning procedure running through a range of wedge angles from very wide to narrow. This is repeated for a range of positions of first leg and apex related to varying CCT’s of the daylight, E1, , En.

Fig. 5
Fig. 5

Test objects’ pixel-points chromaticity distribution. In column (a) both light sources give body and surface reflection. In column (b) the reddish light sources give only body reflection. In column (c) both light sources give only body reflection. The two white dots in all plots show the locations of the two light sources’ chromaticities.

Fig. 6
Fig. 6

Reflectance of the red enameled cup (solid curve) and the blue vacuum jug (dashed curve) obtained by a spectrometer designed and manufactured by VTT, Oulu, Finland.

Fig. 7
Fig. 7

Location of primary chromaticities for the red enameled cup and the blue vacuum jug. Solid and dashed lines connect primary chromaticities due to light sources E1 (Philips TLD 965) and E2 (Philips TLD 927), respectively.

Fig. 8
Fig. 8

Reflectance characteristic used for modeling the body reflection of vegetation (derived from Ref. 16).

Fig. 9
Fig. 9

Comparison of modeled and experimentally derived daylight locus for the camera. (a) Relationship between CCT and r chromaticity. (b) Relationship between r and g chromaticity; since the modeled and the experimentally derived loci are overlying each other, only the modeled locus is included.

Fig. 10
Fig. 10

Comparison of descriptors for the presegmented and the not-presegmented images. (a) Relation between the generalized variance, R2=0.99, (b) relation between the mean value of the r chromaticity of b(E1) and b(E2), R2=0.96.

Fig. 11
Fig. 11

Images of barley acquired at 6:50 a.m. with sunshine and unclouded skylight as illumination sources. Left, class A, with a generalized variance of 441, a mean value of rbody of 0.37, and a CCT of 4750 K. Right, class D, acquired immediately after with a cardboard casting a shadow over the image field, with a generalized variance of 21, a mean value of rbody of 0.31, and a CCT of 14500 K.

Tables (2)

Tables Icon

Table 1 Pixel-Point Distribution Area Due to Reflections Produced by Illumination Sources Ea

Tables Icon

Table 2 Result of Linear Discriminant Analysis for Presegmented and Not-Presegmented Images

Equations (6)

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

L(Θ, λ)=mB(Θ)LB(λ)+mS(Θ)LS(λ),
Cf=L(Θ, λ)τf (λ)s(λ) dλ,
C(x, y)=mB(Θ)CB+mS(Θ)CS,
C(x, y)=mB(Θ)E(Θ, λ)ρB(λ)τf(λ)s(λ)dλ+mS(Θ)E(Θ, λ)ρS(λ)τf(λ)s(λ)dλ.
r3=S1S1+S2r1+S2S1+S2r2,
g3=S1S1+S2g1+S2S1+S2g2,

Metrics