OSA's Digital Library

Virtual Journal for Biomedical Optics

Virtual Journal for Biomedical Optics

| EXPLORING THE INTERFACE OF LIGHT AND BIOMEDICINE

  • Editor: Gregory W. Faris
  • Vol. 3, Iss. 8 — Aug. 18, 2008

Unsupervised illuminant estimation from natural scenes: an RGB digital camera suffices

Juan L. Nieves, Clara Plata, Eva M. Valero, and Javier Romero  »View Author Affiliations


Applied Optics, Vol. 47, Issue 20, pp. 3574-3584 (2008)
http://dx.doi.org/10.1364/AO.47.003574


View Full Text Article

Enhanced HTML    Acrobat PDF (5260 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

A linear pseudo-inverse method for unsupervised illuminant recovery from natural scenes is presented. The algorithm, which uses a digital RGB camera, selects the naturally occurring bright areas (not necessarily the white ones) in natural images and converts the RGB digital counts directly into the spectral power distribution of the illuminants using a learning-based spectral procedure. Computations show a good spectral and colorimetric performance when only three sensors (a three-band RGB camera) are used. These results go against previous findings concerning the recovery of spectral reflectances and radiances, which claimed that the greater the number of sensors, the better the spectral performance. Combining the device with the appropriate computations can yield spectral information about objects and illuminants simultaneously, avoiding the need for spectroradiometric measurements. The method works well and needs neither a white reference located in the natural scene nor direct measurements of the spectral power distribution of the light.

© 2008 Optical Society of America

OCIS Codes
(150.0150) Machine vision : Machine vision
(150.2950) Machine vision : Illumination
(330.1710) Vision, color, and visual optics : Color, measurement

ToC Category:
Machine Vision

History
Original Manuscript: February 4, 2008
Revised Manuscript: May 23, 2008
Manuscript Accepted: May 30, 2008
Published: July 3, 2008

Virtual Issues
Vol. 3, Iss. 8 Virtual Journal for Biomedical Optics

Citation
Juan L. Nieves, Clara Plata, Eva M. Valero, and Javier Romero, "Unsupervised illuminant estimation from natural scenes: an RGB digital camera suffices," Appl. Opt. 47, 3574-3584 (2008)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=ao-47-20-3574


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. B. A. Wandell, “The synthesis and analysis of color images,” IEEE Trans. Pattern Anal. Mach. Intell. 9, 2-13 (1987). [CrossRef] [PubMed]
  2. J. Y. Hardeberg, “Acquisition and reproduction of color images: colorimetric and multispectral approaches,” Ph.D. dissertation (Ecole Nationale Superieure des Telecommunications, 1999).
  3. M. Ebner, Color Constancy (Wiley, 2007).
  4. S. Tominaga, “Multichannel vision system for estimating surface and illumination functions,” J. Opt. Soc. Am. A 13, 2163-2173 (1996). [CrossRef]
  5. F. H. Imai and R. Berns, “Spectral estimation using trichromatic digital cameras,” in Proceedings of the International Symposium on Multispectral Imaging and Color Reproduction for Digital Archives (Society of Multispectral Imaging of Japan, 1999), pp. 42-49.
  6. C.-C. Chiao, D. Osorio, M. Vorobyev, and T. W. Cronin, “Characterization of natural illuminants in forests and the use of digital video data to reconstruct illuminant spectra,” J. Opt. Soc. Am. A 17, 1713-11721 (2000). [CrossRef]
  7. S. M. C. Nascimento, F. P. Ferreira, and D. H. Foster, “Statistics of spatial cone excitation ratios in natural scenes,” J. Opt. Soc. Am. A 19, 1484-1490 (2002). [CrossRef]
  8. V. Cheung, S. Westland, C. Li, J. Hardeberg, and D. Connah, “Characterization of trichromatic color cameras by using a new multispectral imaging technique,” J. Opt. Soc. Am. A 22, 1231-1240 (2005). [CrossRef]
  9. J. L. Nieves, E. M. Valero, S. M. C. Nascimento, J. Hernández-Andrés, and J. Romero, “Multispectral synthesis of daylight using a commercial digital CCD camera,” Appl. Opt. 44, 5696-5703 (2005). [CrossRef] [PubMed]
  10. N. Shimano, “Evaluation of a multispectral image acquisition system aimed at reconstruction of spectral reflectances,” Opt. Eng. 44, 107005 (2005). [CrossRef]
  11. H.-L. Shen, J. H. Xin, and S.-J. Shao, “Improved reflectance reconstruction for multispectral imaging by combining different techniques,” Opt. Express 15, 5531-5536 (2007). [CrossRef] [PubMed]
  12. S. Tominaga and B. A. Wandell, “Standard surface-reflectance model and illuminant estimation,” J. Opt. Soc. Am. A 6, 576-584 (1989). [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. S. Tominaga, “Natural image database and its use for scene illuminant estimation,” J. Electron. Imaging 11, 434-444 (2002). [CrossRef]
  15. S. Tominaga and B. A. Wandell, “Natural scene-illuminant estimation using the sensor correlation,” in Proc. IEEE 90, 42-56 (2002). [CrossRef]
  16. E. M. Valero, J. L. Nieves, S. M. C. Nascimento, K. Amano, and D. H. Foster, “Recovering spectral data from natural scenes with an RGB digital camera,” Color Res. Appl. 32, 352-360(2007). [CrossRef]
  17. J. L. Nieves, E. M. Valero, J. Hernández-Andrés, and J. Romero, “Recovering fluorescent spectra of lights with an RGB digital camera and color filters using different matrix factorizations,” Appl. Opt. 46, 4144-4154 (2007). [CrossRef] [PubMed]
  18. M. D'Zmura and G. Iverson, “Color constancy. I. Basic theory of two-stage linear recovery of spectral descriptions for lights and surfaces,” J. Opt. Soc. Am. A 10, 2148-2165(1993). [CrossRef]
  19. D. H. Brainard and W. T. Freeman, “Bayesian color constancy,” J. Opt. Soc. Am. A 14, 1393-1411 (1997). [CrossRef]
  20. G. Shaefer and S. Hordley, “A combined physical and statistical approach to color constancy,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) (IEEE Computer Society, 2005), Vol. 1, pp. 148-153. [CrossRef]
  21. S. D. Hordley and G. D. Finlayson, “Reevaluation of color constancy algorithm performance,” J. Opt. Soc. Am. A 23, 1008-1020 (2006). [CrossRef]
  22. G. D. Finlayson, S. D. Hordley, and P. M. Hubel, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Machine Intell. 23, 1209-1221(2001). [CrossRef]
  23. D. A. Forsyth, “A novel algorithm for colour constancy,” Int. J. Comput. Vision 5, 5-36 (1990). [CrossRef]
  24. S. A. Shafer, “Using color to separate reflection components,” Color Res. Appl. 10, 210-218 (1985). [CrossRef]
  25. M. A. López-Álvarez, J. Hernández-Andrés, E. 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]
  26. M. Mosny and B. Funt, “Multispectral color constancy: real image tests” Proc. SPIE 6492, 64920S (2007). [CrossRef]
  27. N. Otsu, “A threshold selection method from grey-level histograms,” IEEE Trans. Syst. Man. Cybern. 9, 62-66 (1979). [CrossRef]
  28. G. Schaefer, “Robust dichromatic colour constancy,” in Image Analysis and Recognition. Part II, A. Campilho and M. Kamel, eds. (Springer2004), pp. 257-264.
  29. G. Buchsbaum, “A spatial processor model for object color perception,” J. Franklin Inst. 310, 1-26 (1980). [CrossRef]
  30. V. C. Cardei and B. Funt, “Committee-based color constancy,” in Proceedings of the IS&T/SID Seventh Color Imaging Conference (Society for Imaging Science and Technology, 1999), pp. 311-313.
  31. F. H. Imai, M. R. Rosen, and R. S. Berns, “Comparative study of metrics for spectral match quality,” in Proceedings of the First European Conference on Colour in Graphics, Imaging and Vision (Society for Imaging Science and Technology (2002), pp. 492-496.
  32. M. J. Vrhel, R. Gershon, and L. S. Iwan, “Measurement and analysis of object reflectance spectra,” Color Res. Appl. 19, 4-9 (1994).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited