Abstract

A physics-based method for shadow compensation in scenes illuminated by daylight is proposed. If the daylight is represented by a simplified form of the blackbody law and the camera filters are of infinitely narrow bandwidth, the relationship between red/blue (rm) and green/blue (gm) ratios as the blackbody’s temperature changes is a simple power law where the exponent is independent of the surface reflectivity. When the CIE daylight model is used instead of the blackbody and finite bandwidths for the camera are assumed, it is shown that the power law still holds with a slight change to the exponent. This means that images can be transformed into a map of rm/gmA and then thresholded to yield a shadow-independent classification. Exponent A can be precalculated from the CIE daylight model and the camera filter characteristics. Results are shown for four outdoor images that contain sunny and shadowed parts with vegetation and background. It is shown that the gray-level distributions of the pixels in the transformed images are quite similar for a given component whether or not it is in shadow. The transformation leads to bimodal histograms from which thresholds can easily be selected to give good classifications.

© 2000 Optical Society of America

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References

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    [CrossRef]

1999 (2)

N. D. Tillett, T. Hague, “Computer-vision-based hoe guidance for cereals—an initial trial,” J. Agric. Eng. Res. 74, 225–236 (1999).
[CrossRef]

M. S. Drew, J. Wei, Z. Li, “Illumination invariant image retrieval and video segmentation,” Pattern Recogn. 32, 1369–1388 (1999).
[CrossRef]

1998 (1)

G. L. Foresti, “Object detection and tracking in time-varying and badly illuminated outdoor environments,” Opt. Eng. 37, 2550–2564 (1998).
[CrossRef]

1997 (1)

K. Barnard, G. Finlayson, B. Funt, “Color constancy for scenes with varying illumination,” Comput. Vision Image Understand. 65, 311–321 (1997).
[CrossRef]

1996 (4)

S. Perrin, T. Redarce, “CCD camera modelling and simulation,” J. Intell. Robot. Syst. 17, 309–325 (1996).
[CrossRef]

R. Brivot, J. A. Marchant, “Segmentation of plants and weeds using infrared images,” Proc. Inst. Electr. Eng. 143, 118–124 (1996).

S. Nakauchi, K. Takebe, S. Usui, “A computational model for color constancy by separating reflectance and illuminant edges within a scene,” Neural Networks 9, 1405–1415 (1996).
[CrossRef] [PubMed]

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

1994 (3)

1993 (1)

J. D. Crisman, C. E. Thorpe, “SCARF: a color vision system that tracks roads and intersections,” IEEE Trans. Rob. Autom. 9, 49–57 (1993).
[CrossRef]

1990 (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]

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]

1988 (1)

G. J. Klinker, S. A. Shafer, T. Kanade, “The measurement of highlights in color images,” Int. J. Comput. Vision. 2, 7–32 (1988).
[CrossRef]

1986 (2)

1985 (1)

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

1982 (1)

G. West, M. H. Brill, “Necessary and sufficient conditions for von Kries chromatic adaptation to give color constancy,” J. Math. Biol. 15, 249–258 (1982).
[CrossRef] [PubMed]

1976 (1)

A. W. Hooper, G. O. Harries, B. Ambler, “A photoelectric sensor for distinguishing between plant material and soil,” J. Agric. Eng. Res. 21, 145–155 (1976).
[CrossRef]

1971 (1)

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

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]

Ambler, B.

A. W. Hooper, G. O. Harries, B. Ambler, “A photoelectric sensor for distinguishing between plant material and soil,” J. Agric. Eng. Res. 21, 145–155 (1976).
[CrossRef]

Barnard, K.

K. Barnard, G. Finlayson, B. Funt, “Color constancy for scenes with varying illumination,” Comput. Vision Image Understand. 65, 311–321 (1997).
[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]

Blake, A.

D. Reynard, A. Wildenberg, A. Blake, J. A. Marchant, “Learning dynamics of complex motions from image sequences,” in Proceedings of the 4th European Conference on Computer Vision (Springer, Berlin, 1996), pp. 357–368.

G. Brelstaff, A. Blake, “Detecting specular reflections using Lambertian constraints,” in Proceedings of the 2nd International Conference on Computer Vision (Institute of Electrical and Electronics Engineers, New York, 1988), pp. 297–302.

Brelstaff, G.

G. Brelstaff, A. Blake, “Detecting specular reflections using Lambertian constraints,” in Proceedings of the 2nd International Conference on Computer Vision (Institute of Electrical and Electronics Engineers, New York, 1988), pp. 297–302.

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]

Brill, M. H.

G. West, M. H. Brill, “Necessary and sufficient conditions for von Kries chromatic adaptation to give color constancy,” J. Math. Biol. 15, 249–258 (1982).
[CrossRef] [PubMed]

Brivot, R.

R. Brivot, J. A. Marchant, “Segmentation of plants and weeds using infrared images,” Proc. Inst. Electr. Eng. 143, 118–124 (1996).

Crisman, J. D.

J. D. Crisman, C. E. Thorpe, “SCARF: a color vision system that tracks roads and intersections,” IEEE Trans. Rob. Autom. 9, 49–57 (1993).
[CrossRef]

Drew, M. S.

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]

Finlayson, G.

K. Barnard, G. Finlayson, B. Funt, “Color constancy for scenes with varying illumination,” Comput. Vision Image Understand. 65, 311–321 (1997).
[CrossRef]

Finlayson, G. D.

Foresti, G. L.

G. L. Foresti, “Object detection and tracking in time-varying and badly illuminated outdoor environments,” Opt. Eng. 37, 2550–2564 (1998).
[CrossRef]

Funt, B.

K. Barnard, G. Finlayson, B. Funt, “Color constancy for scenes with varying illumination,” Comput. Vision Image Understand. 65, 311–321 (1997).
[CrossRef]

Funt, B. F.

Gershon, R.

Hague, T.

N. D. Tillett, T. Hague, “Computer-vision-based hoe guidance for cereals—an initial trial,” J. Agric. Eng. Res. 74, 225–236 (1999).
[CrossRef]

T. Hague, J. A. Marchant, N. D. Tillett, “A system for plant scale husbandry,” in Proceedings of the 1st European Conference on Precision Agriculture (BIOS Scientific, Oxford, UK, 1997), pp. 635–642.

Harries, G. O.

A. W. Hooper, G. O. Harries, B. Ambler, “A photoelectric sensor for distinguishing between plant material and soil,” J. Agric. Eng. Res. 21, 145–155 (1976).
[CrossRef]

Hooper, A. W.

A. W. Hooper, G. O. Harries, B. Ambler, “A photoelectric sensor for distinguishing between plant material and soil,” J. Agric. Eng. Res. 21, 145–155 (1976).
[CrossRef]

Jepson, A. D.

Judd, D. B.

Kanade, T.

G. J. Klinker, S. A. Shafer, T. Kanade, “The measurement of highlights in color images,” Int. J. Comput. Vision. 2, 7–32 (1988).
[CrossRef]

Klinker, G. J.

G. J. Klinker, S. A. Shafer, T. Kanade, “The measurement of highlights in color images,” Int. J. Comput. Vision. 2, 7–32 (1988).
[CrossRef]

Lee, H. C.

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]

Li, Z.

M. S. Drew, J. Wei, Z. Li, “Illumination invariant image retrieval and video segmentation,” Pattern Recogn. 32, 1369–1388 (1999).
[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.

Marchant, J. A.

R. Brivot, J. A. Marchant, “Segmentation of plants and weeds using infrared images,” Proc. Inst. Electr. Eng. 143, 118–124 (1996).

T. Hague, J. A. Marchant, N. D. Tillett, “A system for plant scale husbandry,” in Proceedings of the 1st European Conference on Precision Agriculture (BIOS Scientific, Oxford, UK, 1997), pp. 635–642.

D. Reynard, A. Wildenberg, A. Blake, J. A. Marchant, “Learning dynamics of complex motions from image sequences,” in Proceedings of the 4th European Conference on Computer Vision (Springer, Berlin, 1996), pp. 357–368.

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]

Nakauchi, S.

S. Nakauchi, K. Takebe, S. Usui, “A computational model for color constancy by separating reflectance and illuminant edges within a scene,” Neural Networks 9, 1405–1415 (1996).
[CrossRef] [PubMed]

Norman, J. M.

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

Perrin, S.

S. Perrin, T. Redarce, “CCD camera modelling and simulation,” J. Intell. Robot. Syst. 17, 309–325 (1996).
[CrossRef]

Redarce, T.

S. Perrin, T. Redarce, “CCD camera modelling and simulation,” J. Intell. Robot. Syst. 17, 309–325 (1996).
[CrossRef]

Reynard, D.

D. Reynard, A. Wildenberg, A. Blake, J. A. Marchant, “Learning dynamics of complex motions from image sequences,” in Proceedings of the 4th European Conference on Computer Vision (Springer, Berlin, 1996), pp. 357–368.

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.

G. J. Klinker, S. A. Shafer, T. Kanade, “The measurement of highlights in color images,” Int. J. Comput. Vision. 2, 7–32 (1988).
[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]

Stiles, W. S.

G. Wyszecki, W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd ed. (Wiley, New York, 1982).

Takebe, K.

S. Nakauchi, K. Takebe, S. Usui, “A computational model for color constancy by separating reflectance and illuminant edges within a scene,” Neural Networks 9, 1405–1415 (1996).
[CrossRef] [PubMed]

Thorpe, C. E.

J. D. Crisman, C. E. Thorpe, “SCARF: a color vision system that tracks roads and intersections,” IEEE Trans. Rob. Autom. 9, 49–57 (1993).
[CrossRef]

Tillett, N. D.

N. D. Tillett, T. Hague, “Computer-vision-based hoe guidance for cereals—an initial trial,” J. Agric. Eng. Res. 74, 225–236 (1999).
[CrossRef]

T. Hague, J. A. Marchant, N. D. Tillett, “A system for plant scale husbandry,” in Proceedings of the 1st European Conference on Precision Agriculture (BIOS Scientific, Oxford, UK, 1997), pp. 635–642.

Tominaga, S.

Tsotsos, J. K.

Usui, S.

S. Nakauchi, K. Takebe, S. Usui, “A computational model for color constancy by separating reflectance and illuminant edges within a scene,” Neural Networks 9, 1405–1415 (1996).
[CrossRef] [PubMed]

Walter-Shea, E. A.

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

Wandell, B. A.

Wei, J.

M. S. Drew, J. Wei, Z. Li, “Illumination invariant image retrieval and video segmentation,” Pattern Recogn. 32, 1369–1388 (1999).
[CrossRef]

West, G.

G. West, M. H. Brill, “Necessary and sufficient conditions for von Kries chromatic adaptation to give color constancy,” J. Math. Biol. 15, 249–258 (1982).
[CrossRef] [PubMed]

Wildenberg, A.

D. Reynard, A. Wildenberg, A. Blake, J. A. Marchant, “Learning dynamics of complex motions from image sequences,” in Proceedings of the 4th European Conference on Computer Vision (Springer, Berlin, 1996), pp. 357–368.

Wooley, J. T.

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

Wyszecki, G.

G. Wyszecki, W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd ed. (Wiley, New York, 1982).

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. (1)

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

Comput. Vision Image Understand. (1)

K. Barnard, G. Finlayson, B. Funt, “Color constancy for scenes with varying illumination,” Comput. Vision Image Understand. 65, 311–321 (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]

IEEE Trans. Rob. Autom. (1)

J. D. Crisman, C. E. Thorpe, “SCARF: a color vision system that tracks roads and intersections,” IEEE Trans. Rob. Autom. 9, 49–57 (1993).
[CrossRef]

Int. J. Comput. Vision. (1)

G. J. Klinker, S. A. Shafer, T. Kanade, “The measurement of highlights in color images,” Int. J. Comput. Vision. 2, 7–32 (1988).
[CrossRef]

J. Agric. Eng. Res. (2)

N. D. Tillett, T. Hague, “Computer-vision-based hoe guidance for cereals—an initial trial,” J. Agric. Eng. Res. 74, 225–236 (1999).
[CrossRef]

A. W. Hooper, G. O. Harries, B. Ambler, “A photoelectric sensor for distinguishing between plant material and soil,” J. Agric. Eng. Res. 21, 145–155 (1976).
[CrossRef]

J. Intell. Robot. Syst. (1)

S. Perrin, T. Redarce, “CCD camera modelling and simulation,” J. Intell. Robot. Syst. 17, 309–325 (1996).
[CrossRef]

J. Math. Biol. (1)

G. West, M. H. Brill, “Necessary and sufficient conditions for von Kries chromatic adaptation to give color constancy,” J. Math. Biol. 15, 249–258 (1982).
[CrossRef] [PubMed]

J. Opt. Soc. Am. (1)

J. Opt. Soc. Am. A (7)

Neural Networks (1)

S. Nakauchi, K. Takebe, S. Usui, “A computational model for color constancy by separating reflectance and illuminant edges within a scene,” Neural Networks 9, 1405–1415 (1996).
[CrossRef] [PubMed]

Opt. Eng. (1)

G. L. Foresti, “Object detection and tracking in time-varying and badly illuminated outdoor environments,” Opt. Eng. 37, 2550–2564 (1998).
[CrossRef]

Pattern Recogn. (1)

M. S. Drew, J. Wei, Z. Li, “Illumination invariant image retrieval and video segmentation,” Pattern Recogn. 32, 1369–1388 (1999).
[CrossRef]

Plant Physiol. (1)

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

Proc. Inst. Electr. Eng. (1)

R. Brivot, J. A. Marchant, “Segmentation of plants and weeds using infrared images,” Proc. Inst. Electr. Eng. 143, 118–124 (1996).

Other (9)

T. Hague, J. A. Marchant, N. D. Tillett, “A system for plant scale husbandry,” in Proceedings of the 1st European Conference on Precision Agriculture (BIOS Scientific, Oxford, UK, 1997), pp. 635–642.

D. Reynard, A. Wildenberg, A. Blake, J. A. Marchant, “Learning dynamics of complex motions from image sequences,” in Proceedings of the 4th European Conference on Computer Vision (Springer, Berlin, 1996), pp. 357–368.

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

G. Wyszecki, W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd ed. (Wiley, New York, 1982).

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

Commission Internationale de L’Eclairage (CIE) “Colorimetry,” Tech. Rep.2nd ed. (CIE, Paris, 1986).

G. Brelstaff, A. Blake, “Detecting specular reflections using Lambertian constraints,” in Proceedings of the 2nd International Conference on Computer Vision (Institute of Electrical and Electronics Engineers, New York, 1988), pp. 297–302.

G. E. Healey, S. A. Shafer, L. B. Wolff, eds., Physics-Based Vision: Principles and Practice (Jones & Bartlett, Boston, Mass., 1992).

J. D. E. Beynon, D. R. Lamb, eds., Charge Coupled Devices and Their Application (McGraw-Hill, London, 1980).

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Figures (8)

Fig. 1
Fig. 1

Spectral sensitivity (arbitrary units) of the three bands of the M90 camera.

Fig. 2
Fig. 2

Spectral reflectance of typical vegetation and soil and of a third material (blue shingle; see Ref. 24).

Fig. 3
Fig. 3

CIE daylight standard at several CCT’s. Also shown are blackbody spectral content and an approximate form of the blackbody equation. Note that the blackbody and approximate forms merge at the lowest CCT.

Fig. 4
Fig. 4

sr versus sg for CIE daylight, assuming infinitely narrow camera filter bands. Also shown is the curve sr=sg1.51.

Fig. 5
Fig. 5

rm versus gm calculated for vegetation and soil with the M90 camera at three CCT’s. Also shown are examples of the function rm=Fgm1.51 for F=0.75, 1.05.

Fig. 6
Fig. 6

Images exhibiting sunny and shadow components. Top, sel5; upper middle, sel8; lower middle jtse_000; bottom jsse_000. Left, original images; middle images of rm/gm1.51; right, classified images.

Fig. 7
Fig. 7

Manual classification of images sel5 (left) and sel8 (right). From lightest to darkest: vegetation sun, background sun, vegetation shade, background shade, “don’t know.”

Fig. 8
Fig. 8

Histograms of transformed images (rm/gm1.51). Left, sel5; right, sel8. Rows from top to bottom: vegetation sun, vegetation shadow, background sun, background shadow, total.

Tables (4)

Tables Icon

Table 1 Scaling Factors for Various Illumination Models

Tables Icon

Table 2 Scaling Factors for Vegetation, Soil, and Blue Shingle for Daylight with the M90 Camera

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Table 3 Mean Value and Spread of the Function rm/gm1.51 As the Viewing Direction Was Changed for Images of Tall Wheat Approximately Half in Sun and Half in Shadow

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Table 4 Spatial Variation of the Mean Value and Spread of the Function rm/gm1.51 for an Image of Tall Wheat Approximately Half in Sun and Half in Shadow

Equations (16)

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CI=GISI(λ)ρ(λ)E(λ)dλ,
Meλ=c1λ-5[exp(c2/Tλ)-1]-1,
Meλ=c1λ-5exp(-c2/Tλ)
CI=gIρ(λcI)E(λcI),
CIref=CIm Eref (λcI)/Em(λcI),
rref=rm/sr,gref=gm/sg,
sr=Eref (λcB)Em(λcR)Eref (λcR)Em(λcB),sg=Eref (λcB)Em(λcG)Eref (λcG)Em(λcB).
sr=expc21Tref-1Tm1λcR-1λcB,
sg=expc21Tref-1Tm1λcG-1λcB,
sr=sgA,
A=1/λcR-1/λcB1/λcG-1/λcB.
rm=FgmA,
F=rref/grefA.
rref=gRρ(λcR)Eref (λcR)gBρ(λcB)Eref (λcB),gref=gGρ(λcG)Eref (λcG)gBρ(λcB)Eref (λcB).
λcI=SI(λ)λdλSI(λ)dλ,
A=log srlog sg,

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