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基于不同色块数量的光谱重构对比
引用本文:丁国华,朱元泓,李博,梁权峰.基于不同色块数量的光谱重构对比[J].包装工程,2012,33(3):14-18.
作者姓名:丁国华  朱元泓  李博  梁权峰
作者单位:南京林业大学汀苏省制浆造纸科学与技术重点实验室,南京210037/深圳职业技术学院,深圳518000;深圳职业技术学院,深圳,518000;曲阜师范大学,日照,276800;南京林业大学汀苏省制浆造纸科学与技术重点实验室,南京,210037
基金项目:深圳市科技计划项目(JC200903180754A)
摘    要:分别对24色块、140色块、1 269色块3种不同颜色数量的哑光色卡光谱反射率进行主成分分析(PCA),利用分析所提取的最大6个特征向量重构光谱,并使用均方根误差值(RMSE)和CIE1976色差值对结果进行评价。实验结果表明:同等条件重构后,140色卡在累计贡献率和色差方面均优于另外2种数量的色卡,均方根误差也仅次于1 269色卡,表明不同色块数量的训练样本会影响光谱重构精度;重构后的光谱反射率均在对应黄色、绿色、蓝色等的中间波段,再现效果最好,蓝紫色相对较弱,橙色-红色表现最差。

关 键 词:多光谱成像  主成分分析  光谱重构
收稿时间:2011/6/10 0:00:00
修稿时间:2012/2/10 0:00:00

Comparison of Spectrum Reconstruction on Different Number of Color Block
DING Guo-hu,ZHU Yuan-hong,LI Bo and LIANG Quan-feng.Comparison of Spectrum Reconstruction on Different Number of Color Block[J].Packaging Engineering,2012,33(3):14-18.
Authors:DING Guo-hu  ZHU Yuan-hong  LI Bo and LIANG Quan-feng
Affiliation:1(1.Jiangsu Provincial Key Lab of Pulp and Paper Science and Technology,Nanjing Forestry University,Nanjing 210037,China;2.Shenzhen Polytechnic,Shenzhen 518000,China;3.Qufu Normal University,Qufu 276800,China)
Abstract:The spectral reflectance of the 24,140,1269 matt Munsell color cards were analyzed respectively by principal component analysis,and then spectral reconstruction was carried out using 6 biggest eigenvectors extracted by the analysis.The results were evaluated by using RMSE and color difference value of CIE1976.The reconstruction results showed that 140 color cards surpass other two kinds of quantities in accumulative total contribution rate and color difference under the same reconstruction condition;RMSE of the 140 color cards is inferior to that of the 1269 color cards,which suggests that the number of training sample has influence on reconstruction precision;reconstructed spectral reflectance is in the middle band of yellow,green,and blue,which represent the best reproduction;the reproduction performance of blue-purple is not as good and that of orange-red performance is the worst.
Keywords:multispectral imaging  principal component analysis  spectrum reconstruction
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