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《Journal of Forestry Engineering》 2018-01
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Classification for decorative papers of wood-based panels using color and glossiness parameters in combination with neural network method

LI Kang;ZHANG Maomao;YANG Zhong;LYU Bin;Research Institute of Wood Industry,Chinese Academy of Forestry;  
As a main decorative material applied to wood-based panel,there is a great demand for decorative paper.However,the quality problem of decorative paper,such as chromatic aberration,has been of a concern in the decorative paper industry. The traditional color difference evaluation method,such as artificial visual assessment,was prone to be affected by subjective factors and the efficiency of this method was relatively low. To explore the possibility of using visual parameters,which included lightness( L*),index of( red-green opponent) axis( a*),index of( yellow-blue opponent) axis( b*),and glossiness( G) to quantitatively analyze the surface characteristics of decorative papers and to classify the different types of decorative papers,and the feature information of visual parameters was extracted to establish classification model. The models of the principal component analysis( PCA) and back-propagation( BP)network were developed to distinguish different types of decorative papers in this study. To avoid overfitting,full-cross validation was applied for PCA modeling of samples. Based on BP network,levenberg-marquardt( L-M) algorithm was selected for the adjustment of weights and thresholds in order to reduce goal error of parameter vector. The results showed that the classification of different types of decorative papers worked well and the total accuracy reached 80.9%when the parameters of L*,a*and b*were employed. The classification accuracy increased to 92.9% when the parameter of glossiness was added,indicating that the model is accurate and practical for the quantitative analysis and rapid classification of decorative papers using the parameters of L*,a*,b*and G coupled with BP network.
【Fund】: 国家自然科学基金(31370711);; 国家重点研发计划项目(2016YFD0600706)
【CateGory Index】: TS761
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