Abstract

In this paper, the results of an investigation of the possibility of extending “color constancy” to obtain illuminant-invariant reflectance features from data in the near-ultraviolet (UV) and near-infrared (IR) wavelength regions are reported. These features are obtained by extending a blackbody-model-based color constancy algorithm proposed by Ratnasingam and Collins [J. Opt. Soc. Am. A 27, 286 (2010)] to these additional wavelengths. Ratnasingam and Collins applied the model-based algorithm in the visible region to extract two illuminant-invariant features related to the wavelength-dependent reflectance of a surface from the responses of four sensors. In this paper, this model-based algorithm is extended to extract two illuminant-invariant reflectance features from the responses of sensors that cover the visible and either the near-UV or near-IR wavelength. In this investigation, test reflectance data sets are generated using the goodness–fitness coefficient (GFC). The appropriateness of the GFC for generating the test data sets is demonstrated by comparing the results obtained with these data with those obtained from data sets generated using the CIELab distance. Results based upon the GFC are then presented that suggest that the model-based algorithm can extract useful features from data from the visible and near-IR wavelengths. Finally, results are presented that show that, although the spectrum of daylight in the near UV is very different from a blackbody spectrum, the algorithm can be modified to extract useful features from visible and near-UV wavelengths.

© 2011 Optical Society of America

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References

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  1. J. A. Worthey and M. H. Brill, “Heuristic analysis of von Kries color constancy,” J. Opt. Soc. Am. A 1708–1720(1986).
    [CrossRef] [PubMed]
  2. M. Ebner, Color Constancy, Wiley–IS&T Series in Imaging Science and Technology (Wiley, 2007).
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    [CrossRef] [PubMed]
  4. J. A. Marchant and C. M. Onyango, “Shadow-invariant classification for scenes illuminated by daylight,” J. Opt. Soc. Am. A 17, 1952–1961, 2000.
    [CrossRef]
  5. G. D. Finlayson and S. D. Hordley, “Color constancy at a pixel,” J. Opt. Soc. Am. A 18, 253–264, 2001.
    [CrossRef]
  6. G. D. Finlayson and M. S. Drew, “4-sensor camera calibration for image representation invariant to shading, shadows, lighting, and specularities,” in Proceedings of the Eighth IEEE International Conference on Computer Vision (IEEE2001), pp. 473–480.
    [CrossRef]
  7. S. Ratnasingam and S. Collins “Study of the photodetector characteristics of a camera for color constancy in natural scenes,” J. Opt. Soc. Am. A 27, 286–294, 2010.
    [CrossRef]
  8. http://www.ccrs.nrcan.gc.ca/resource/tutor/fundam/chapter4/01_e.php.
  9. S. Ratnasingam, S. Collins, and J. Hernández-Andrés, “Optimum sensors for color constancy in scenes illuminated by daylight,” J. Opt. Soc. Am. A 27, 2198–2207 (2010).
    [CrossRef]
  10. S. Winkler and S. Susstrunk, “Visibility of noise in natural images,” Proc. SPIE 5292, 121–129 (2004).
    [CrossRef]
  11. “Database—Munsell colors matt,” http://cs.joensuu.fi/~spectral/databases/download/munsell_spec_matt.htm.
  12. S. E. J. Arnold, V. Savolainen, and L. Chittka, “FReD: the floral reflectance spectra database,” Nat. Prec., http://dx.doi.org/10.1038/npre.2008.1846.1 (2008).
    [CrossRef]
  13. J. Hernández-Andrés, J. Romero, J. L. Nieves, and R. L. Lee, Jr., “Color and spectral analysis of daylight in southern Europe,” J. Opt. Soc. Am. A 18, 1325–1335 (2001).
    [CrossRef]
  14. J. Romero, A. García-Beltrán, and J. Hernández-Andrés, “Linear bases for representation of natural and artificial illuminants,” J. Opt. Soc. Am. A 14, 1007–1014 (1997).
    [CrossRef]
  15. F. H. Imai, M. R. Rosen, and R. S. Berns, “Comparative study of metrics for spectral match quality,” in Proceedings of CGIV 2002: The First European Conference on Colour in Graphics Image and Vision (Society for Imaging Science and Technology, 2002), pp. 492–496.
  16. H. Laamanen, T. Jetsu, T. Jaaskelainen, and J. Parkkinen, “Weighted compression of spectral color information,” J. Opt. Soc. Am. A 25, 1383–1388 (2008).
    [CrossRef]
  17. M. A. López-Álvarez, J. Hernández-Andrés, Eva. M. Valero, and J. Romero, “Selecting algorithms, sensors and linear bases for optimum spectral recovery of skylight,” J. Opt. Soc. Am. A 24, 942–956 (2007).
    [CrossRef]

2010 (2)

2008 (1)

2007 (1)

2004 (1)

S. Winkler and S. Susstrunk, “Visibility of noise in natural images,” Proc. SPIE 5292, 121–129 (2004).
[CrossRef]

2001 (2)

2000 (1)

1997 (1)

1986 (1)

J. A. Worthey and M. H. Brill, “Heuristic analysis of von Kries color constancy,” J. Opt. Soc. Am. A 1708–1720(1986).
[CrossRef] [PubMed]

1971 (1)

Arnold, S. E. J.

S. E. J. Arnold, V. Savolainen, and L. Chittka, “FReD: the floral reflectance spectra database,” Nat. Prec., http://dx.doi.org/10.1038/npre.2008.1846.1 (2008).
[CrossRef]

Berns, R. S.

F. H. Imai, M. R. Rosen, and R. S. Berns, “Comparative study of metrics for spectral match quality,” in Proceedings of CGIV 2002: The First European Conference on Colour in Graphics Image and Vision (Society for Imaging Science and Technology, 2002), pp. 492–496.

Brill, M. H.

J. A. Worthey and M. H. Brill, “Heuristic analysis of von Kries color constancy,” J. Opt. Soc. Am. A 1708–1720(1986).
[CrossRef] [PubMed]

Chittka, L.

S. E. J. Arnold, V. Savolainen, and L. Chittka, “FReD: the floral reflectance spectra database,” Nat. Prec., http://dx.doi.org/10.1038/npre.2008.1846.1 (2008).
[CrossRef]

Collins, S.

Drew, M. S.

G. D. Finlayson and M. S. Drew, “4-sensor camera calibration for image representation invariant to shading, shadows, lighting, and specularities,” in Proceedings of the Eighth IEEE International Conference on Computer Vision (IEEE2001), pp. 473–480.
[CrossRef]

Ebner, M.

M. Ebner, Color Constancy, Wiley–IS&T Series in Imaging Science and Technology (Wiley, 2007).

Finlayson, G. D.

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

G. D. Finlayson and M. S. Drew, “4-sensor camera calibration for image representation invariant to shading, shadows, lighting, and specularities,” in Proceedings of the Eighth IEEE International Conference on Computer Vision (IEEE2001), pp. 473–480.
[CrossRef]

García-Beltrán, A.

Hernández-Andrés, J.

Hordley, S. D.

Imai, F. H.

F. H. Imai, M. R. Rosen, and R. S. Berns, “Comparative study of metrics for spectral match quality,” in Proceedings of CGIV 2002: The First European Conference on Colour in Graphics Image and Vision (Society for Imaging Science and Technology, 2002), pp. 492–496.

Jaaskelainen, T.

Jetsu, T.

Laamanen, H.

Land, E. H.

Lee, R. L.

López-Álvarez, M. A.

Marchant, J. A.

McCann, J. J.

Nieves, J. L.

Onyango, C. M.

Parkkinen, J.

Ratnasingam, S.

Romero, J.

Rosen, M. R.

F. H. Imai, M. R. Rosen, and R. S. Berns, “Comparative study of metrics for spectral match quality,” in Proceedings of CGIV 2002: The First European Conference on Colour in Graphics Image and Vision (Society for Imaging Science and Technology, 2002), pp. 492–496.

Savolainen, V.

S. E. J. Arnold, V. Savolainen, and L. Chittka, “FReD: the floral reflectance spectra database,” Nat. Prec., http://dx.doi.org/10.1038/npre.2008.1846.1 (2008).
[CrossRef]

Susstrunk, S.

S. Winkler and S. Susstrunk, “Visibility of noise in natural images,” Proc. SPIE 5292, 121–129 (2004).
[CrossRef]

Valero, Eva. M.

Winkler, S.

S. Winkler and S. Susstrunk, “Visibility of noise in natural images,” Proc. SPIE 5292, 121–129 (2004).
[CrossRef]

Worthey, J. A.

J. A. Worthey and M. H. Brill, “Heuristic analysis of von Kries color constancy,” J. Opt. Soc. Am. A 1708–1720(1986).
[CrossRef] [PubMed]

J. Opt. Soc. Am. (1)

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

J. A. Marchant and C. M. Onyango, “Shadow-invariant classification for scenes illuminated by daylight,” J. Opt. Soc. Am. A 17, 1952–1961, 2000.
[CrossRef]

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

J. A. Worthey and M. H. Brill, “Heuristic analysis of von Kries color constancy,” J. Opt. Soc. Am. A 1708–1720(1986).
[CrossRef] [PubMed]

S. Ratnasingam and S. Collins “Study of the photodetector characteristics of a camera for color constancy in natural scenes,” J. Opt. Soc. Am. A 27, 286–294, 2010.
[CrossRef]

S. Ratnasingam, S. Collins, and J. Hernández-Andrés, “Optimum sensors for color constancy in scenes illuminated by daylight,” J. Opt. Soc. Am. A 27, 2198–2207 (2010).
[CrossRef]

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

J. Romero, A. García-Beltrán, and J. Hernández-Andrés, “Linear bases for representation of natural and artificial illuminants,” J. Opt. Soc. Am. A 14, 1007–1014 (1997).
[CrossRef]

H. Laamanen, T. Jetsu, T. Jaaskelainen, and J. Parkkinen, “Weighted compression of spectral color information,” J. Opt. Soc. Am. A 25, 1383–1388 (2008).
[CrossRef]

M. A. López-Álvarez, J. Hernández-Andrés, Eva. M. Valero, and J. Romero, “Selecting algorithms, sensors and linear bases for optimum spectral recovery of skylight,” J. Opt. Soc. Am. A 24, 942–956 (2007).
[CrossRef]

Proc. SPIE (1)

S. Winkler and S. Susstrunk, “Visibility of noise in natural images,” Proc. SPIE 5292, 121–129 (2004).
[CrossRef]

Other (6)

“Database—Munsell colors matt,” http://cs.joensuu.fi/~spectral/databases/download/munsell_spec_matt.htm.

S. E. J. Arnold, V. Savolainen, and L. Chittka, “FReD: the floral reflectance spectra database,” Nat. Prec., http://dx.doi.org/10.1038/npre.2008.1846.1 (2008).
[CrossRef]

F. H. Imai, M. R. Rosen, and R. S. Berns, “Comparative study of metrics for spectral match quality,” in Proceedings of CGIV 2002: The First European Conference on Colour in Graphics Image and Vision (Society for Imaging Science and Technology, 2002), pp. 492–496.

http://www.ccrs.nrcan.gc.ca/resource/tutor/fundam/chapter4/01_e.php.

M. Ebner, Color Constancy, Wiley–IS&T Series in Imaging Science and Technology (Wiley, 2007).

G. D. Finlayson and M. S. Drew, “4-sensor camera calibration for image representation invariant to shading, shadows, lighting, and specularities,” in Proceedings of the Eighth IEEE International Conference on Computer Vision (IEEE2001), pp. 473–480.
[CrossRef]

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Figures (7)

Fig. 1
Fig. 1

Typical boundary of a reflectance pair from Munsell reflectance test set: (a) boundaries go through the midpoint of the two clusters’ mean points and (b) boundaries drawn with 90% of the Mahalanobis distance ( D m 1 and D m 2 ). The Gaussian sensor responses were multiplied by 100 samples of 40 dB noise. In the figures, a square represents the points generated by one member of the reflectance pair, and a cross represents the points generated by the other member of the reflectance pair.

Fig. 2
Fig. 2

Power spectra distribution of four of the measured daylight.

Fig. 3
Fig. 3

Power spectra of blackbody illuminant ( 6557 K ) and measured daylight ( 6557 K ). Both spectra are normalized at 550 nm .

Fig. 4
Fig. 4

Performance of the model-based algorithm when applying the reflectance data sets generated using GFC and CIELab distance: (a) Munsell and (b) floral. The algorithm was investigated by applying the data in the wavelength range of 400 to 700 nm .

Fig. 5
Fig. 5

Separability test results with Munsell and measured daylight. In this test, the model-based four-sensor algorithm was tested with visible data alone and visible and near-IR data together.

Fig. 6
Fig. 6

Separability test results with floral and measured daylight. In this test, the model-based algorithm was tested with visible data alone and visible and UV data together.

Fig. 7
Fig. 7

Performance of the model-based algorithm with floral and measured daylight when tested with visible data alone and visible and UV data together with GFC values of (a) 0.995 and (b) 0.999. The performance of the algorithm with calculated coefficients (listed in Table 2) in the respective wavelength ranges and also the performance of the algorithm with an alpha value of 0.8 (optimum alpha). In all three cases, the channel coefficient gamma was kept the same as calculated (see Table 2).

Tables (2)

Tables Icon

Table 1 Goodness-Fitness Coefficient Range of the Munsell and Floral Test Reflectance Data Sets for Different Wavelength Ranges

Tables Icon

Table 2 Channel Coefficients of the Model-Based Algorithm for Different Wavelength Ranges Calculated Using Eqs. (3, 4)

Equations (7)

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

F 1 = log ( R 2 ) { α log ( R 1 ) + ( 1 α ) log ( R 3 ) } ,
F 2 = log ( R 3 ) { γ log ( R 2 ) + ( 1 γ ) log ( R 4 ) } ,
1 λ 2 = α λ 1 + 1 α λ 3 ,
1 λ 3 = γ λ 2 + 1 γ λ 4 ,
D m 1 2 = ( P C 1 ) Σ 1 1 ( P C 1 ) ,
D m 2 2 = ( P C 2 ) Σ 2 1 ( P C 2 ) ,
GFC = | j E E ( λ j ) E R ( λ j ) | | j [ E E ( λ i ) ] 2 | 1 / 2 | j [ E R ( λ j ) ] 2 | 1 / 2 ,

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