本文選題：人造板裝飾紙　切入點：表面色澤特征分類　出處：《林業(yè)工程學(xué)報》2018年01期

【摘要】：通過測定人造板專用裝飾紙表面的色澤參數(shù)(色度學(xué)參數(shù)和光澤度),對裝飾紙表面色澤特征進(jìn)行量化分析,并利用色澤參數(shù)的特征信息結(jié)合誤差反向傳播神經(jīng)網(wǎng)絡(luò)(BP神經(jīng)網(wǎng)絡(luò))對裝飾紙進(jìn)行建模分類,探討利用裝飾紙表面的色澤參數(shù)進(jìn)行裝飾紙表面色澤特征分類。以色澤參數(shù)數(shù)據(jù)作為神經(jīng)網(wǎng)絡(luò)的輸入變量,裝飾紙類型作為神經(jīng)網(wǎng)絡(luò)的輸出變量,建立三層BP神經(jīng)網(wǎng)絡(luò)模型,其中,隱含層的最佳節(jié)點數(shù)為9。結(jié)果表明:通過對色澤度參數(shù)的主成分分析,增加了光澤度參數(shù)后,各類裝飾紙之間的獨特性增強,更利于對裝飾紙進(jìn)行分類。利用色度學(xué)參數(shù)(明度指數(shù)L*,紅綠軸色品指數(shù)a*和黃藍(lán)軸色品指數(shù)b*)對裝飾紙進(jìn)行建模分類時,判別的總正確率為80.9%,引入光澤度參數(shù)之后判別的總正確率提高至92.9%,說明利用色度學(xué)與光澤度參數(shù)結(jié)合BP神經(jīng)網(wǎng)絡(luò)可以用于裝飾紙表面視覺特征的量化分析以及快速識別分類。
[Abstract]:By measuring the color parameters (chromaticity parameters and glossiness) on the surface of decorative paper for wood-based panels, the color characteristics of decorative paper surface were analyzed quantitatively.Using the characteristic information of color parameters and error back propagation neural network (BP neural network), the paper models and classifies decorative paper, and discusses how to classify the color characteristics of decorative paper surface by using the color parameters of decorative paper surface.Using color parameter data as input variable of neural network and decorative paper type as output variable of neural network, a three-layer BP neural network model is established, in which the best number of nodes in hidden layer is 9.The results showed that by the principal component analysis of color and lustre parameters, the uniqueness of all kinds of decorative paper was enhanced with the addition of gloss parameters, and it was more favorable to classify decorative paper.In the modeling and classification of decorative paper, the colorimetric parameters (brightness index, red and green axis color index a * and yellow and blue axis color product index b) were used to model and classify decorative paper.The total correct rate of discrimination is 80.9, and the total correct rate of discriminating is increased to 92.9 after introducing the gloss parameter. It shows that the combination of chrominance and glossiness parameters combined with BP neural network can be used for the quantitative analysis of the visual features of decorative paper surface and the fast recognition and classification.
【作者單位】： 中國林業(yè)科學(xué)研究院木材工業(yè)研究所;
【基金】：國家自然科學(xué)基金(31370711) 國家重點研發(fā)計劃項目(2016YFD0600706)
【分類號】：TS761

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本文編號：1704022