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.
© 2002 Optical Society of America
Robert Piché, "Nonnegative color spectrum analysis filters from principal component analysis characteristic spectra," J. Opt. Soc. Am. A 19, 1946-1950 (2002)