关键词: 彩色扫描仪, 光谱特征化, 多项式回归, BP神经网络, 主成分分析

Abstract:

Spectral characterization of color scanners can achieve accurate obtaining of color information of scanners in the different light sources and observers. It can also avoid metamerism to the most extent. Spectral characterization was applied to characterize the color scanners. Principal component analysis, which was combined with polynomial regression and BP neural network technology, set up the nonlinear transformation relationship between the scanner RGB signal and spectral reflectance image information. Firstly, principal component analysis (PCA) was used to analyze the spectral reflectance of the training sample followed by the principal component scalars calculation and the spectral was represented by principal component times scalars of principal component. Conversion models for scalars of principal component and polynomial or BP neural network was built by experiments. The reflectance was built by RGB low-dimension signal and spectral characterization of color scanners was achieved. Experimental results showed that the better number of polynomial terms was 19, reaching the accuracy of 1.7% RMSE of training sample and 1.9% RMSE of test sample. And the optimum network structure was single hidden layer with 15 layer node, reaching the accuracy of 1.3% RMSE of training sample and 1.5% RMSE of test sample. The accuracy of polynomial regression method is much lower, which is not fit for spectral characterization. The BP neural network model may achieve higher spectral characterization accuracy.

Key words: color scanners, spectral characterization, polynomial regression, BP neural network, principal component analysis