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Journal of the Optical Society of America A

Journal of the Optical Society of America A


  • Vol. 16, Iss. 9 — Sep. 1, 1999
  • pp: 2169–2176

Independent-component analysis of skin color image

Norimichi Tsumura, Hideaki Haneishi, and Yoichi Miyake  »View Author Affiliations

JOSA A, Vol. 16, Issue 9, pp. 2169-2176 (1999)

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The spatial distributions of melanin and hemoglobin in human skin are separated by independent-component analysis of a skin color image. The analysis is based on the skin color model with three assumptions: (1) Spatial variation of color in the skin is caused by two pigments, melanin and hemoglobin; (2) the quantities of the two pigments are mutually independent spatially; and (3) linearity holds among the quantities and the observed color signals in the optical density domain. The results of the separation agree well with physiological knowledge. The separated components are synthesized to simulate the various facial color images by changing the quantities of the two separated pigments.

© 1999 Optical Society of America

OCIS Codes
(100.2960) Image processing : Image analysis
(330.1690) Vision, color, and visual optics : Color

Original Manuscript: January 13, 1999
Manuscript Accepted: April 28, 1999
Published: September 1, 1999

Norimichi Tsumura, Hideaki Haneishi, and Yoichi Miyake, "Independent-component analysis of skin color image," J. Opt. Soc. Am. A 16, 2169-2176 (1999)

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  1. R. W. G. H. Hunt, The Reproduction of Colour (Fountain, London, 1995).
  2. F. H. Imai, N. Tsumura, H. Haneishi, Y. Miyake, “Principal component analysis of skin color and its application to colorimetric color reproduction on CRT display and hardcopy,” J. Imaging Sci. Technol. 40, 422–430 (1996).
  3. Y. Yokoyama, N. Tsumura, H. Haneishi, Y. Miyake, J. Hayashi, M. Saito, “A new color management system based on human perception and its application to recording and reproduction of art printing,” in Proceedings of IS&T/SID 5th Color Imaging Conference, Color Science, Systems and Applications (Society for Imaging Science & Technology, Springfield, Va., 1997), pp. 169–172.
  4. P. Hanarahan, W. Krueger, “Reflection from layered surfaces due to subsurface scattering,” Proceedings of SIGGRAPH 93 (Association for Computing Machinery, New York, 1993), pp. 165–174.
  5. M. Yamaguchi, R. Iwama, Y. Ohya, T. Obi, N. Ohyama, Y. Komiya, “Natural color reproduction in the television system for telemedicine,” in Medical Imaging 1997: Image Display, Y. Kim, ed., Proc. SPIE3031, 482–489 (1997). [CrossRef]
  6. M. J. C. van Gemert, S. L. Jacques, H. J. C. M. Sternborg, W. M. Star, “Skin Optics,” IEEE Trans. Biomed. Eng. 36, 1146–1154 (1989). [CrossRef] [PubMed]
  7. R. R. Anderson, J. A. Parrish, “The optics in human skin,” Invest. Dermatol. 77, 13–19 (1981). [CrossRef]
  8. E. A. Edwards, S. Q. Duntley, “The pigments and color of living human skin,” Am. J. Anat. 65, 1–33 (1939). [CrossRef]
  9. J. Karhunen, A. Hyvarinen, R. Vigário, J. Hurri, E. Oja, “Applications of neural blind separation to signal and image processing,” in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE Computer Society Press, Los Alamitos, Calif., 1997), Vol. 1, pp. 131–134.
  10. T. Inoue, Y. Fujii, K. Itoh, Y. Ichioka, “Block blind separation of independent spectra in hyperspectral-images,” in 1996 International Topical Meeting on Optical Computing: Technical Digest (Japan Society for Applied Physics, Tokyo, 1996), Vol. 1, pp. 40–41.
  11. G. Burel, “Blind separation of sources: a nonlinear neural algorithm,” Neural Networks 5, 937–947 (1992). [CrossRef]
  12. A. Hyvärinen, E. Oja, “A fast fixed-point algorithm for independent component analysis,” Neural Comput. 9, 1483–1492 (1997). [CrossRef]
  13. C. Jutten, J. Hearult, “Blind separation of sources, part I: an adaptive algorithm based on neuromimetic architecture,” Signal Process. 24, 1–10 (1991). [CrossRef]
  14. J. Karhunen, E. Oja, L. Wang, R. Vigário, J. Joutensalo, “A class of neural networks for independent component analysis,” IEEE Trans. Neural Netw. 8, 486–504 (1997). [CrossRef] [PubMed]
  15. H. H. Yang, S. Amari, “Adaptive online learning algorithms for blind separation: maximum entropy and minimum mutual information,” Neural Comput. 9, 1457–1482 (1997). [CrossRef]
  16. M. Hiraoka, M. Firbank, M. Essenpreis, M. Cope, S. R. Arridge, P. v. d. Zee, D. T. Delpy, “A Monte Carlo investigation of optical pathlength in inhomogeneous tissue and its application to near-infrared spectroscopy,” Phys. Med. Biol. 38, 1859–1876 (1993). [CrossRef] [PubMed]
  17. A. Garce, MatlabOptimization Toolbox User’s Guide (The MathWorks, Boston, Mass., 1992).
  18. P. J. Dwyer, R. R. Anderson, C. A. DiMarzio, “Mapping blood oxygen saturation using a multi-spectral imaging system,” in Biomedical Sensing, Imaging, and Tracking Technolgies II, T. Vo-Dinh, R. A. Lieberman, G. G. Vurek, eds. Proc. SPIE2976, 270–280 (1997). [CrossRef]

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