Abstract

Essential to sensory processing in the human visual system is natural illumination, which can vary considerably not only across space but also along the day depending on the atmospheric conditions and the sun’s position in the sky. In this work, edges derived from the three postreceptoral Luminance, Red–Green, and Blue–Yellow signals were computed from hyperspectral images of natural scenes rendered with daylights of Correlated Color Temperatures (CCTs) from 2735 to 25,889 K; for low CCT, the same analysis was performed using Planckian illuminants up to 800 K. It was found that average luminance and chromatic edge contrasts were maximal for low correlated color temperatures and almost constants above 10,000 K. The magnitude of these contrast changes was, however, only about 2% across the tested daylights. Results suggest that the postreceptoral opponent and nonopponent color vision mechanisms produce almost constant responses for color edge detection under natural illumination.

© 2012 Optical Society of America

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References

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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
  17. P. Ricchiazzi, S. Yang, C. Gautier, and D. Sowle, “SBDART: a research and teaching software tool for plane-parallel radiative transfer in the earth’s atmosphere,” Bull. Am. Meteorol. Soc.2101–2114 (1998).
    [CrossRef]
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    [CrossRef]
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    [CrossRef]
  28. I. Fine, D. I. A. MacLeod, and G. M. Boynton, “Surface segmentation based on the luminance and color statistics of natural scenes,” J. Opt. Soc. Am. A 20, 1283–1291 (2003).
    [CrossRef]

2011 (1)

D. H. Foster, “Color constancy,” Vis. Res. 51, 674–700(2011).
[CrossRef]

2009 (1)

T. Hansen and K. R. Gegenfurtner, ”Independence of color and luminance edges in natural scenes,” Vis. Neurosci. 26, 35–49 (2009).
[CrossRef]

2008 (3)

W. S. Geisler, “Visual perception and the statistical properties of natural scenes,” Ann. Rev. Psychol. 59, 167–192 (2008).
[CrossRef]

S. K. Shevell and F. A. A. Kingdom, “Color in complex scenes,” Ann. Rev. Psychol. 59, 143–166 (2008).
[CrossRef]

C. Zhou and B. W. Mel, “Cue combination and color edge detection in natural scenes,” J. Vis. 8(4), 4, 1–25 (2008).
[CrossRef]

2006 (1)

2005 (3)

2004 (1)

D. H. Foster, S. M. C. Nascimento, and K. Amano, “Information limits on neural identification of coloured surfaces in natural scenes,” Vis. Neurosci. 21, 331–336 (2004).
[CrossRef]

2003 (1)

2002 (2)

2001 (2)

E. P. Simoncelli and B. A. Olshausen, “Natural image statistics and neural representation,” Ann. Rev. Neurosci. 24, 1193–1216 (2001).
[CrossRef]

J. Hernández-Andrés, J. Romero, J. L. Nieves, and R. L. Lee, “Color and spectral analysis of daylight in southern Europe,” J. Opt. Soc. Am. A 18, 1325–1335 (2001).
[CrossRef]

2000 (2)

R. M. Balboa and N. M. Grzywacz, “Occlusions and their relationship with the distribution of contrasts in natural images,” Vis. Res. 40, 2661–2669 (2000).
[CrossRef]

P. Sumner and J. D. Mollon, “Chromaticity as a signal of ripeness in fruits taken by primates,” J. Exp. Biol. 203, 1987–2000 (2000).

1998 (3)

1993 (2)

1978 (1)

1975 (1)

V. C. Smith and J. Pokorny, “Spectral sensitivity of the foveal cone photopigments between 400 nm and 500 nm,” Vis. Res. 15, 161–171 (1975).
[CrossRef]

Amano, K.

D. H. Foster, K. Amano, S. M. C. Nascimento, and M. J. Foster, “Frequency of metamerism in natural scenes,” J. Opt. Soc. Am. A 23, 2359–2372 (2006).
[CrossRef]

D. H. Foster, S. M. C. Nascimento, and K. Amano, “Information limits on neural identification of coloured surfaces in natural scenes,” Vis. Neurosci. 21, 331–336 (2004).
[CrossRef]

Baddeley, R.

Baker, C. L.

Balboa, R. M.

R. M. Balboa and N. M. Grzywacz, “Occlusions and their relationship with the distribution of contrasts in natural images,” Vis. Res. 40, 2661–2669 (2000).
[CrossRef]

Barlow, H. B.

H. B. Barlow, “Possible principles underlying the transformation of sensory messages,” in Sensory Communication (MIT, 1961), pp. 217–234.

Boynton, G. M.

Brelstaff, G.

Chiao, C.-C.

Cole, G. R.

Cronin, T. W.

DeValois, K. K.

R. L. DeValois and K. K. DeValois, “A multi-stage color model,” Vis. Res. 33, 1053–1065 (1993).
[CrossRef]

DeValois, R. L.

R. L. DeValois and K. K. DeValois, “A multi-stage color model,” Vis. Res. 33, 1053–1065 (1993).
[CrossRef]

Ferreira, F.

Fine, I.

Foster, D. H.

D. H. Foster, “Color constancy,” Vis. Res. 51, 674–700(2011).
[CrossRef]

D. H. Foster, K. Amano, S. M. C. Nascimento, and M. J. Foster, “Frequency of metamerism in natural scenes,” J. Opt. Soc. Am. A 23, 2359–2372 (2006).
[CrossRef]

D. H. Foster, S. M. C. Nascimento, and K. Amano, “Information limits on neural identification of coloured surfaces in natural scenes,” Vis. Neurosci. 21, 331–336 (2004).
[CrossRef]

S. M. C. Nascimento, F. Ferreira, and D. H. Foster, “Statistics of spatial cone-excitation ratios in natural scenes,” J. Opt. Soc. Am. A 19, 1484–1490 (2002).
[CrossRef]

Foster, M. J.

Gautier, C.

P. Ricchiazzi, S. Yang, C. Gautier, and D. Sowle, “SBDART: a research and teaching software tool for plane-parallel radiative transfer in the earth’s atmosphere,” Bull. Am. Meteorol. Soc.2101–2114 (1998).
[CrossRef]

Gazzaniga, M.

M. Gazzaniga, The New Cognitive Neurosciences, 2nd ed. (MIT, 2000).

Gegenfurtner, K. R.

T. Hansen and K. R. Gegenfurtner, ”Independence of color and luminance edges in natural scenes,” Vis. Neurosci. 26, 35–49 (2009).
[CrossRef]

Geisler, W. S.

W. S. Geisler, “Visual perception and the statistical properties of natural scenes,” Ann. Rev. Psychol. 59, 167–192 (2008).
[CrossRef]

Golz, J.

J. Golz and D. I. A. MacLeod, “Influence of scene statistics on color constancy,” Nature 415, 637–640 (2002).
[CrossRef]

Grzywacz, N. M.

R. M. Balboa and N. M. Grzywacz, “Occlusions and their relationship with the distribution of contrasts in natural images,” Vis. Res. 40, 2661–2669 (2000).
[CrossRef]

Hansen, T.

T. Hansen and K. R. Gegenfurtner, ”Independence of color and luminance edges in natural scenes,” Vis. Neurosci. 26, 35–49 (2009).
[CrossRef]

Hernández-Andrés, J.

Hine, T.

Hoyer, P. O.

A. Hyvarinen, J. Hurri, and P. O. Hoyer, Natural Image Statistics: A Probabilistic Approach to Early Computational Vision (Springer, 2009).

Hurri, J.

A. Hyvarinen, J. Hurri, and P. O. Hoyer, Natural Image Statistics: A Probabilistic Approach to Early Computational Vision (Springer, 2009).

Hyvarinen, A.

A. Hyvarinen, J. Hurri, and P. O. Hoyer, Natural Image Statistics: A Probabilistic Approach to Early Computational Vision (Springer, 2009).

Johnson, A. P.

Kingdom, F. A. A.

Lee, R. L.

Leonards, U.

Lovell, P. G.

MacLeod, D. I. A.

Mangoubi, S. S.

McIlhagga, W.

Mel, B. W.

C. Zhou and B. W. Mel, “Cue combination and color edge detection in natural scenes,” J. Vis. 8(4), 4, 1–25 (2008).
[CrossRef]

Mollon, J. D.

P. Sumner and J. D. Mollon, “Chromaticity as a signal of ripeness in fruits taken by primates,” J. Exp. Biol. 203, 1987–2000 (2000).

Moorehead, I. R.

Nascimento, S. M. C.

Nieves, J. L.

Olshausen, B. A.

E. P. Simoncelli and B. A. Olshausen, “Natural image statistics and neural representation,” Ann. Rev. Neurosci. 24, 1193–1216 (2001).
[CrossRef]

Párraga, C. A.

Pokorny, J.

V. C. Smith and J. Pokorny, “Spectral sensitivity of the foveal cone photopigments between 400 nm and 500 nm,” Vis. Res. 15, 161–171 (1975).
[CrossRef]

Ricchiazzi, P.

P. Ricchiazzi, S. Yang, C. Gautier, and D. Sowle, “SBDART: a research and teaching software tool for plane-parallel radiative transfer in the earth’s atmosphere,” Bull. Am. Meteorol. Soc.2101–2114 (1998).
[CrossRef]

Romero, J.

Ruderman, D. L.

Shevell, S. K.

S. K. Shevell and F. A. A. Kingdom, “Color in complex scenes,” Ann. Rev. Psychol. 59, 143–166 (2008).
[CrossRef]

Simoncelli, E. P.

E. P. Simoncelli and B. A. Olshausen, “Natural image statistics and neural representation,” Ann. Rev. Neurosci. 24, 1193–1216 (2001).
[CrossRef]

Smith, V. C.

V. C. Smith and J. Pokorny, “Spectral sensitivity of the foveal cone photopigments between 400 nm and 500 nm,” Vis. Res. 15, 161–171 (1975).
[CrossRef]

Sowle, D.

P. Ricchiazzi, S. Yang, C. Gautier, and D. Sowle, “SBDART: a research and teaching software tool for plane-parallel radiative transfer in the earth’s atmosphere,” Bull. Am. Meteorol. Soc.2101–2114 (1998).
[CrossRef]

Sumner, P.

P. Sumner and J. D. Mollon, “Chromaticity as a signal of ripeness in fruits taken by primates,” J. Exp. Biol. 203, 1987–2000 (2000).

Tolhurst, D. J.

Troscianko, J.

Troscianko, T.

Yang, S.

P. Ricchiazzi, S. Yang, C. Gautier, and D. Sowle, “SBDART: a research and teaching software tool for plane-parallel radiative transfer in the earth’s atmosphere,” Bull. Am. Meteorol. Soc.2101–2114 (1998).
[CrossRef]

Zeevi, Y. Y.

Zhou, C.

C. Zhou and B. W. Mel, “Cue combination and color edge detection in natural scenes,” J. Vis. 8(4), 4, 1–25 (2008).
[CrossRef]

Ann. Rev. Neurosci. (1)

E. P. Simoncelli and B. A. Olshausen, “Natural image statistics and neural representation,” Ann. Rev. Neurosci. 24, 1193–1216 (2001).
[CrossRef]

Ann. Rev. Psychol. (2)

W. S. Geisler, “Visual perception and the statistical properties of natural scenes,” Ann. Rev. Psychol. 59, 167–192 (2008).
[CrossRef]

S. K. Shevell and F. A. A. Kingdom, “Color in complex scenes,” Ann. Rev. Psychol. 59, 143–166 (2008).
[CrossRef]

Appl. Opt. (1)

Bull. Am. Meteorol. Soc. (1)

P. Ricchiazzi, S. Yang, C. Gautier, and D. Sowle, “SBDART: a research and teaching software tool for plane-parallel radiative transfer in the earth’s atmosphere,” Bull. Am. Meteorol. Soc.2101–2114 (1998).
[CrossRef]

J. Exp. Biol. (1)

P. Sumner and J. D. Mollon, “Chromaticity as a signal of ripeness in fruits taken by primates,” J. Exp. Biol. 203, 1987–2000 (2000).

J. Opt. Soc. Am. (1)

J. Opt. Soc. Am. A (9)

A. P. Johnson, F. A. A. Kingdom, and C. L. Baker, “Spatiochromatic statistics of natural scenes: first- and second-order information and their correlational structure,” J. Opt. Soc. Am. A 22, 2050–2059 (2005).
[CrossRef]

I. Fine, D. I. A. MacLeod, and G. M. Boynton, “Surface segmentation based on the luminance and color statistics of natural scenes,” J. Opt. Soc. Am. A 20, 1283–1291 (2003).
[CrossRef]

D. L. Ruderman, T. W. Cronin, and C.-C. Chiao, “Statistics of cone responses to natural images: implications for visual coding,” J. Opt. Soc. Am. A 15, 2036–2045 (1998).
[CrossRef]

C. A. Párraga, G. Brelstaff, T. Troscianko, and I. R. Moorehead, “Color and luminance information in natural scenes,” J. Opt. Soc. Am. A 15, 563–569 (1998).
[CrossRef]

G. R. Cole, T. Hine, and W. McIlhagga, “Detection mechanisms in L-, M-, and S-cone contrast space,” J. Opt. Soc. Am. A 10, 38–51 (1993).
[CrossRef]

P. G. Lovell, D. J. Tolhurst, C. A. Párraga, R. Baddeley, U. Leonards, J. Troscianko, and T. Troscianko, “Stability of the color-opponent signals under changes of illuminant in natural scenes,” J. Opt. Soc. Am. A 22, 2060–2071 (2005).
[CrossRef]

S. M. C. Nascimento, F. Ferreira, and D. H. Foster, “Statistics of spatial cone-excitation ratios in natural scenes,” J. Opt. Soc. Am. A 19, 1484–1490 (2002).
[CrossRef]

D. H. Foster, K. Amano, S. M. C. Nascimento, and M. J. Foster, “Frequency of metamerism in natural scenes,” J. Opt. Soc. Am. A 23, 2359–2372 (2006).
[CrossRef]

J. Hernández-Andrés, J. Romero, J. L. Nieves, and R. L. Lee, “Color and spectral analysis of daylight in southern Europe,” J. Opt. Soc. Am. A 18, 1325–1335 (2001).
[CrossRef]

J. Vis. (1)

C. Zhou and B. W. Mel, “Cue combination and color edge detection in natural scenes,” J. Vis. 8(4), 4, 1–25 (2008).
[CrossRef]

Nature (1)

J. Golz and D. I. A. MacLeod, “Influence of scene statistics on color constancy,” Nature 415, 637–640 (2002).
[CrossRef]

Vis. Neurosci. (2)

T. Hansen and K. R. Gegenfurtner, ”Independence of color and luminance edges in natural scenes,” Vis. Neurosci. 26, 35–49 (2009).
[CrossRef]

D. H. Foster, S. M. C. Nascimento, and K. Amano, “Information limits on neural identification of coloured surfaces in natural scenes,” Vis. Neurosci. 21, 331–336 (2004).
[CrossRef]

Vis. Res. (4)

R. M. Balboa and N. M. Grzywacz, “Occlusions and their relationship with the distribution of contrasts in natural images,” Vis. Res. 40, 2661–2669 (2000).
[CrossRef]

V. C. Smith and J. Pokorny, “Spectral sensitivity of the foveal cone photopigments between 400 nm and 500 nm,” Vis. Res. 15, 161–171 (1975).
[CrossRef]

R. L. DeValois and K. K. DeValois, “A multi-stage color model,” Vis. Res. 33, 1053–1065 (1993).
[CrossRef]

D. H. Foster, “Color constancy,” Vis. Res. 51, 674–700(2011).
[CrossRef]

Other (4)

M. Gazzaniga, The New Cognitive Neurosciences, 2nd ed. (MIT, 2000).

H. B. Barlow, “Possible principles underlying the transformation of sensory messages,” in Sensory Communication (MIT, 1961), pp. 217–234.

Commission Internationale de l’Eclairage (CIE), Standard S 003/E-1996, Spatial Distribution of Daylight—CIE Standard Overcast Sky and Clear Sky (CIE, Vienna, 1996), p. 3.

A. Hyvarinen, J. Hurri, and P. O. Hoyer, Natural Image Statistics: A Probabilistic Approach to Early Computational Vision (Springer, 2009).

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Figures (6)

Fig. 1.
Fig. 1.

(a) SPDs for daylights of correlated color temperatures between 2735 and 25,889 K. (b) The CIE 1931 x, y chromaticities of the daylight spectra (open circles) overlaid with the Planckian locus (solid line). (c) Examples of the natural scenes that are used for the experiment.

Fig. 2.
Fig. 2.

Overview of the color-opponent edge computation. A hyperspectral image was rendered under each of the daylights and (left column) the three L, M, and S cone planes were obtained; (middle column) the postreceptoral Lum, RG, and BY images were computed, and (right column) edges were detected in each image. All the image planes are shown here in false color.

Fig. 3.
Fig. 3.

Example of the distributions of edge contrasts along the (a) nonopponent and (b)–(c) opponent color mechanisms for three daylights of different CCTs.

Fig. 4.
Fig. 4.

Normalized edge contrasts for the Lum, RG, and BY color mechanisms and natural daylights with correlated color temperatures ranging from 2735 to 25,889 K.

Fig. 5.
Fig. 5.

Normalized edge contrasts for the Lum, RG, and BY color mechanisms and Planckian illuminations with correlated color temperatures ranging from 800 to 2700 K.

Fig. 6.
Fig. 6.

Normalized edge contrasts obtained using adapted receptoral responses to derive the Lum, RG, and BY color mechanisms for different natural daylights.

Tables (1)

Tables Icon

Table 1. Average Statistics for Edge Differences Among All Possible Pairs of Illuminants from 2,735 to 25,889 Ka

Equations (8)

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

L=λ=400700l(λ)r(λ)e(λ)Δλ,M=λ=400700m(λ)r(λ)e(λ)Δλ,S=λ=400700s(λ)r(λ)e(λ)Δλ,
Lum=L+M,RG=LM,BY=2S(L+M).
Ekh=(101202101)*Ik;Ekv=(Ekh)T,
Ek=12(Ekh+Ekv).
Ci,j,kx=|Ei,j,kx|Ii,j,kx,
Ci,k¯=19j=19(1Nx=1NCi,j,kx),
LR=logLlogL¯,MR=logMlogM¯,SR=logSlogS¯,
LumR=LR+MRRGR=LRMRBYR=2SR(LR+MR),

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