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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. 9, Iss. 4 — Apr. 1, 1992
  • pp: 516–520

Reconstruction of Munsell color space by a five-layer neural network

Shiro Usui, Shigeki Nakauchi, and Masae Nakano  »View Author Affiliations


JOSA A, Vol. 9, Issue 4, pp. 516-520 (1992)
http://dx.doi.org/10.1364/JOSAA.9.000516


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Abstract

We have constructed a wine-glass-type five-layer neural network and generated an identity mapping of the surface spectral-reflectance data of 1280 Munsell color chips, using a backpropagation learning algorithm. To achieve an identity mapping, the same data set is used for the input and for the teacher. After the learning was completed, we analyzed the responses to individual chips of the three hidden units in the middle layer in order to obtain the internal representation of the color information. We found that each of the three hidden units corresponds to a psychological color attribute, that is, the Munsell value (luminance), red–green, and yellow–blue. We also examined the relationship between the internal representation and the number of hidden units and found that the network with three hidden units acquires optimum color representation. The five-layer neural network is shown to be an efficient method for reproducing the transformation of color information (or color coding) in the visual system.

© 1992 Optical Society of America

History
Original Manuscript: April 22, 1991
Revised Manuscript: November 5, 1991
Manuscript Accepted: November 7, 1991
Published: April 1, 1992

Citation
Shiro Usui, Shigeki Nakauchi, and Masae Nakano, "Reconstruction of Munsell color space by a five-layer neural network," J. Opt. Soc. Am. A 9, 516-520 (1992)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-9-4-516

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