Meng Da1, Huiqin Wang1,*, Ke Wang1, Zhan Wang2
CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 513-527, 2022, DOI:10.32604/cmes.2022.019040
- 24 January 2022
Abstract The properties of the same pigments in murals are affected by different concentrations and particle diameters, which cause the shape of the spectral reflectance data curve to vary, thus influencing the outcome of matching calculations. This paper proposes a spectral matching classification method of multi-state similar pigments based on feature differences. Fast principal component analysis (FPCA) was used to calculate the eigenvalue variance of pigment spectral reflectance, then applied to the original reflectance values for parameter characterization. We first projected the original spectral reflectance from the spectral space to the characteristic variance space to identify… More >
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