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Nonnegative color spectrum analysis filters from principal component analysis characteristic spectra
1Department of Mathematics, Tampere University of Technology, Finland. robert.piche@tut.fi
Abstract:
Nonnegative color analysis filters are obtained by using an invertible linear transformation of characteristic spectra, which are orthogonal vectors from a principal component analysis (PCA) of a representative ensemble of color spectra. These filters maintain the optimal compression properties of the PCA scheme. Linearly constrained nonlinear programming is used to find a transformation that minimizes the noise sensitivity of the filter set. The method is illustrated by computing analysis and synthesis filters for an ensemble of measured Munsell color spectra.
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