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6. Conclusions
We have addressed the problem of synthesizing a spectral reflectance function
given the standard CIE 1931 tristimulus values and representing the unknown
reflectance functions with linear models. In particular, we have investigated
different types of basis functions for the linear model, and different datasets for
benchmarking. No single set of basis functions can perfectly model all reflectance
functions. Our results confirm that PCA basis sets provide the most effective
approach to the problem. These results also indicate that when a suitable set of
reflectance data for the calculus of PCA basis functions is lacking, a Gaussian basis
set may be satisfactorily employed instead.
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