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Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Vol. 19, Iss. 12 — Dec. 1, 2002
  • pp: 2374–2386

Estimating the scene illumination chromaticity by using a neural network

Vlad C. Cardei, Brian Funt, and Kobus Barnard  »View Author Affiliations


JOSA A, Vol. 19, Issue 12, pp. 2374-2386 (2002)
http://dx.doi.org/10.1364/JOSAA.19.002374


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Abstract

A neural network can learn color constancy, defined here as the ability to estimate the chromaticity of a scene’s overall illumination. We describe a multilayer neural network that is able to recover the illumination chromaticity given only an image of the scene. The network is previously trained by being presented with a set of images of scenes and the chromaticities of the corresponding scene illuminants. Experiments with real images show that the network performs better than previous color constancy methods. In particular, the performance is better for images with a relatively small number of distinct colors. The method has application to machine vision problems such as object recognition, where illumination-independent color descriptors are required, and in digital photography, where uncontrolled scene illumination can create an unwanted color cast in a photograph.

© 2002 Optical Society of America

OCIS Codes
(100.2000) Image processing : Digital image processing
(330.0330) Vision, color, and visual optics : Vision, color, and visual optics
(330.1690) Vision, color, and visual optics : Color
(330.1710) Vision, color, and visual optics : Color, measurement
(330.1720) Vision, color, and visual optics : Color vision

History
Original Manuscript: February 10, 2002
Revised Manuscript: July 2, 2002
Manuscript Accepted: July 2, 2002
Published: December 1, 2002

Citation
Vlad C. Cardei, Brian Funt, and Kobus Barnard, "Estimating the scene illumination chromaticity by using a neural network," J. Opt. Soc. Am. A 19, 2374-2386 (2002)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-19-12-2374


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References

  1. M. J. Swain, D. Ballard, “Color indexing,” Int. J. Comput. Vision 7, 11–32 (1991). [CrossRef]
  2. B. Funt, K. Barnard, L. Martin, “Is color constancy good enough?” in Proceedings of the Fifth European Conference on Computer Vision, H. Burkhardt, B. Neumann, eds. (Springer, Berlin, 1998), pp. 445–459.
  3. D. H. Brainard, B. A. Wandell, “Asymmetric color matching: how color appearance depends on the illuminant,” J. Opt. Soc. Am. A 9, 1433–1448 (1992). [CrossRef] [PubMed]
  4. D. H. Brainard, W. A. Brunt, J. M. Speigle, “Color constancy in the nearly natural image. 1. Asymmetric matches,” J. Opt. Soc. Am. A 14, 2091–2110 (1997). [CrossRef]
  5. G. Finlayson, M. Drew, B. Funt, “Color constancy: generalized diagonal transforms suffice,” J. Opt. Soc. Am. A 11, 3011–3020 (1994). [CrossRef]
  6. J. von Kries, “Chromatic adaptation,” in Sources of Color Vision, D. L. MacAdam, ed. (MIT Press, Cambridge, Mass., 1970). Originally published in Festschrift der Albrecht-Ludwigs-Universitat, 1902), pp. 145–148.
  7. J. von Kries, “Influence of adaptation on the effects produced by luminous stimuli,” in Sources of Color Vision, D. L. MacAdam, ed. (MIT Press, Cambridge, Mass., 1970). Originally published in Handbuch der Physiologie des Menschen, 1905), Vol. 3, pp. 109–282.
  8. J. A. Worthey, M. H. Brill, “Heuristic Analysis of von Kries color constancy,” J. Opt. Soc. Am. A 3, 1708–1712 (1986). [CrossRef] [PubMed]
  9. G. Finlayson, M. Drew, B. Funt, “Spectral sharpening: sensor transformations for improved color constancy,” J. Opt. Soc. Am. A 11, 1553–1563 (1994). [CrossRef]
  10. K. Barnard, F. Ciurea, B. Funt, “Sensor sharpening for computational color constancy,” J. Opt. Soc. Am. A 18, 2728–2743 (2001). [CrossRef]
  11. G. Buchsbaum, “A spatial processor model for object colour perception,” J. Franklin Inst. 310, 1–26 (1980). [CrossRef]
  12. R. Gershon, A. D. Jepson, J. K. Tsotsos, “From [R, G, B] to surface reflectance: computing color constant descriptors in images,” Perception 17, 755–758 (1988).
  13. D. H. Brainard, W. T. Freeman, “Bayesian color constancy,” J. Opt. Soc. Am. A 14, 1393–1411 (1997). [CrossRef]
  14. E. H. Land, J. J. McCann, “Lightness and Retinex theory,” J. Opt. Soc. Am. 61, 1–11 (1971). [CrossRef] [PubMed]
  15. J. Cohen, “Dependency of the spectral reflectance curves of the Munsell color chips,” Psychon. Sci. 1, 369–370 (1964). [CrossRef]
  16. 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]
  17. B. A. Wandell, “The synthesis and analysis of color images,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI9, 2–13 (1987). [CrossRef]
  18. L. Maloney, B. A. Wandell, “Color constancy: a method for recovering surface spectral reflectance,” J. Opt. Soc. Am. A 3, 29–33 (1986). [CrossRef] [PubMed]
  19. G. Finlayson, B. Funt, K. Barnard, “Color constancy under varying illumination,” in Proceedings of the Fifth International Conference on Computer Vision, W. E. L. Grimson, ed. (IEEE Computer Society Press, Los Alamitos, Calif., 1995), pp. 720–725.
  20. D. A. Forsyth, “A novel algorithm for color constancy,” Int. J. Comput. Vision 5, 5–36 (1990). [CrossRef]
  21. G. Finlayson, P. Hubel, S. Hordley, “Color by correlation,” in Proceedings of the IS&T/SID Fifth Color Imaging Conference: Color Science, Systems and Applications, (Society for Imaging Science and Technology, Springfield, Va., 1997), pp. 6–11.
  22. G. Finlayson, S. Hordley, P. Hubel, “Color by correlation: a simple unifying framework for color constancy,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1209–1221 (2001). [CrossRef]
  23. A. C. Hurlbert, T. A. Poggio, “Synthesizing a color algorithm from examples,” Science 239, 482–485 (1988). [CrossRef] [PubMed]
  24. A. C. Hurlbert, “Neural network approaches to color vision,” in Neural Networks for Perception: Vol. 1: Human and Machine Perception, H. Wechsler, ed. (Academic, San Diego, Calif., state, 1991), pp. 266–284.
  25. A. Moore, J. Allman, R. M. Goodman, “A real-time neural system for color constancy,” IEEE Trans. Neural Netw. 2, 237–247 (1991). [CrossRef] [PubMed]
  26. S. Usui, S. Nakauchi, Y. Miyamoto, “A neural network model for color constancy based on the minimally redundant color representation,” in Proceedings of the International Joint Conference on Neural Networks (International Neural Network Society, Mt. Royal, N.J., 1992), Vol. 2, pp. 696–701.
  27. S. M. Courtney, L. F. Finkel, G. Buchsbaum, “A multistage neural network for color constancy and color induction,” IEEE Trans. Neural Netw. 6, 972–985 (1995). [CrossRef] [PubMed]
  28. B. Funt, V. Cardei, K. Barnard, “Learning Color Constancy,” in Proceedings of the IS&T/SID Fourth Color Imaging Conference: Color Science, Systems and Applications (Society for Imaging Science and Technology, Springfield, Va., 1996), pp. 58–60.
  29. K. Barnard, “Practical colour constancy,” Ph.D. thesis (Simon Fraser University, Burnaby, B.C., Canada, 1999).
  30. K. Barnard, V. Cardei, B. Funt, “A comparison of computational color constancy algorithms; part one: methodology and experiments with synthesized data,” IEEE Trans. Image Process. (to be published).
  31. S. Geman, E. Bienenstock, R. Doursat, “Neural networks and the bias/variance dilemma,” Neural Comput. 4, 1–58 (1992). [CrossRef]
  32. D. E. Rumelhart, G. E. Hinton, R. J. Williams, “Learning internal representations by error propagation,” in Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol. I: Foundations, D. E. Rumelhart, J. L. McClelland, and the PDP Research Group, eds. (MIT Press, Cambridge, Mass., 1986), pp. 318–362.
  33. J. Hertz, A. Krogh, R. G. Palmer, Introduction to the Theory of Neural Computation (Addison-Wesley, Reading, Mass., 1991).
  34. V. Cardei, B. Funt, K. Barnard, “Modeling color constancy with neural networks,” in Proceedings of the International Conference on Vision, Recognition, and Action: Neural Models of Mind and Machine (Center for Adaptive Systems, Boston University, Boston, Mass., 1997).
  35. D. Plaut, S. Nowlan, G. Hinton, “Experiments on learning by back propagation,” (Carnegie-Mellon University, Pittsburgh, Pa., 1986).
  36. M. J. Vrhel, R. Gershon, L. S. Iwan, “Measurement and analysis of object reflectance spectra,” Color Res. Appl. 19, 4–9 (1994).
  37. K. Barnard, B. Funt, “Camera characterization for color research,” Color Res. Appl. 27, 153–164 (2002). [CrossRef]
  38. K. Barnard, G. Finlayson, B. Funt, “Color constancy for scenes with varying illumination,” Comput. Vision Image Understand. 65, 311–321 (1997). [CrossRef]
  39. G. Wyszecki, W. Stiles, Color Science: Concepts and Methods, Quantitative data and Formulae, 2nd ed. (Wiley, New York, 1982).
  40. M. Anderson, R. Motta, S. Chandrasekar, M. Stokes, “Proposal for a standard default color space for the Internet–sRGB,” in Proceedings of the IS&T/SID Fourth Color Imaging Conference: Color Science, Systems and Applications (Society for Imaging Science and Technology, Springfield, Va., 1996), pp. 238–246.
  41. G. Finlayson, “Color in perspective,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 1034–1038 (1996). [CrossRef]
  42. B. Funt, V. Cardei, K. Barnard, “Neural network color constancy and specularly reflecting surfaces,” in AIC Color 97 Proceedings of the 8th Congress of the International Colour Association (Color Science Association of Japan, Tokyo, 1997), Vol. II, pp. 523–526.
  43. H. Lee, “Method for computing the scene-illuminant chromaticity from specular highlights,” J. Opt. Soc. Am. A 3, 1694–1699 (1986). [CrossRef] [PubMed]
  44. W. M. Richard, Automatic Detection of Effective Scene Illuminant Chromaticity from Specular Highlights in Digital Images, M.Sc. thesis (Rochester Institute of Technology, Rochester, N.Y., 1995).
  45. S. A. Shafer, “Using color to separate reflection components,” Color Res. Appl. 10, 210–218 (1985). [CrossRef]
  46. V. Cardei, B. Funt, “Color correcting uncalibrated digital images,” J. Imaging Sci. Technol. 44, 288–294 (2000).
  47. R. L. Eubank, Spline Smoothing and Nonparametric Regression (Marcel Dekker, New York, 1988).
  48. J. Moody, “Prediction risk and architecture selection for neural networks,” in From Statistics to Neural Networks: Theory and Pattern Recognition Applications, V. Cherkassky, J. H. Friedman, H. Wechsler, eds., NATO ASI Series F (Springer-Verlag, Berlin, 1994), pp. 147–165.

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