Abstract
We present a new method for producing color filters with positive coefficients to represent color reflectance spectra. The subspace method which is based on the KL-expansion can be used to define a basis to describe the spectral data accurately. However, due the orthogonality of the eigenvectors, the corresponding color filters usually contain negative coefficients and cannot be used in optical components directly. Our method finds the set of vectors which span a very similar color space as the subspace method does. These color filters contain only positive coefficients and can be directly used in optical implementations. We used an unsupervised competitive neural network (Instar) to find a set of positive color filters. The experiments with the Munsell spectra show that the filters produced by the neural network span a color space very similar to the color space spanned by the eigenvectors of the subspace method.
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© 1997 Springer-Verlag Berlin Heidelberg
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Hauta-Kasarill, M., Wang, W., Toyooka, S., Parkkinen, J., Lenz, R. (1997). Unsupervised filtering of Munsell spectra. In: Chin, R., Pong, TC. (eds) Computer Vision — ACCV'98. ACCV 1998. Lecture Notes in Computer Science, vol 1351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63930-6_128
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DOI: https://doi.org/10.1007/3-540-63930-6_128
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