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基于光谱表示和独立成分分析的混合颜料成分分析方法 被引量:11

A Composition Analysis Method of Mixed Pigments Based on Spectrum Expression and Independent Component Analysis
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摘要 在混合颜料成分分析中,反射光谱法通过计算相似性来判断基本颜料种类,容易受多种基本颜料的影响,造成分析结果不准确。将光谱表示为信号,结合独立成分分析,提出一种基于光谱表示和独立成分分析的混合颜料成分分析方法。首先,采用光谱仪获取混合颜料光谱信息,并将其表示为离散信号的形式;然后,对信号进行独立成分分析,得到基本颜料的光谱信息;随后,通过计算基本颜料光谱与已知颜料光谱的相似性,确定基本颜料种类;最后,逆用Kubelka-Munk混色公式就可以得出基本颜料的比例。我们采用蒙赛尔色卡光谱制作模拟数据,分别进行正常/扰动情况下三种色卡光谱混合信息的成分分析试验,以及从8种色卡光谱中选择若干种混合后的成分分析实验。分离出的光谱形态与已知的原始颜料光谱形态极其相似,平均相似比为97.72%,最大相似比可以达到99.95%,得出的基本颜料比例与混合时的比例基本相同。实验结果表明本方法适用于混合颜料成分分析。 Reflectance spectrometry is a common method in composition analysis of mixed pigments.In this method,similarity is used to determine the type of basic pigments that constitute the mixed pigments.But its result may be inaccurate because it is easily influenced by a variety of basic pigments.In this study,a composition analysis method of mixed pigments based on spectrum expression and independent component analysis is proposed,and the composition of mixed pigments can be calculated accurately.First of all,the spectral information of mixed pigments is obtained with spectrometer,and is expressed as the discrete signal.After that,the spectral information of basic pigments is deduced with independent component analysis.Then,the types of basic pigments are determined by calculating the spectrum similarity between the basic pigments and known pigments.Finally,the ratios of basic pigments are obtained by solving the Kubelka-Munk equation system.In addition,the simulated spectrum data of Munsell color card is used to validate this method.The compositions of mixed pigments from three basic pigments are determined under the circumstance of normality and disturbance.And the compositions of mixture from several pigments within the set of eight basic pigments are deduced successfully.The curves of separated pigment spectrums are very similar to the curves of original pigment spectrums.The average similarity is 97.72%,and the maximum one can reach to 99.95%.The calculated ratios of basic pigments close to the original one.It can be seen that this method is suitable for composition analysis of mixed pigments.
出处 《光谱学与光谱分析》 SCIE EI CAS CSCD 北大核心 2015年第6期1682-1689,共8页 Spectroscopy and Spectral Analysis
基金 国家重点基础研究发展规划基金项目(2012CB725304) 国家自然科学基金项目(61373072) 中国博士后科学基金项目(2013M530035)资助
关键词 颜料 成分分析 光谱 相似性 独立成分分析 蒙赛尔 Color Composition analysis Spectrum Similarity Independent component analysis Munsell
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参考文献15

  • 1Tiziana C, Annamaria G, Marco N. Procedia Chemistry, 2013, 8(1): 45.
  • 2ZHANG Sheng-ping(张胜平). Based on the Multispectral Imaging of Research on Method of the Color Rejuvenation of Chinese Paintings(基于多光谱图像的古画颜色修复方法研究),Master Degree Dissertation (硕士论文). Tianjin: Tianjin University(天津: 天津大学), 2010, 8.
  • 3Richard E B, Rand C. Dynamic Programming. New Jersey: Princeton University Press,1957. 35.
  • 4Beyel K, Goldstein J, Ranmakrishnan R. Proceedings of the International Conference on Database Theory, 1999. 217.
  • 5Hyvrinen A, Oja E. Neural computation, 1997, 9(7): 1483.
  • 6Berns R S. Billmeyer and Saltzman’s Principles of Color Technology(颜色技术原理). Translated by LI Xiao-mei, MA Ru, CHEN Li-rong(李小梅, 马 如, 陈立荣, 译). Beijing: Chemical Industry Press(北京: 化学工业出版社), 2002. 34.
  • 7Kubelka P, Munk F. Zeitschrift Fur Technische Physik, 1931, 12(11): 593.
  • 8张贤达,保铮.盲信号分离[J].电子学报,2001,29(z1):1766-1771. 被引量:211
  • 9LIN Qiu-hua(林秋华). Novel Methods ofr Encyrpting Images and Speeches Based on Blind Souree Separation (基于盲源分离的图像与语音加密新方法研究), Doctoral Dissertation(博士论文). Dalian: Dalian University of Technology(大连: 大连理工大学), 2006, 20.
  • 10Sapatnekar S S. IEEE Journal on Emerging and Selected Topics in Circuits and Systems,2011, 1(1): 5.

二级参考文献51

  • 1[1]Amari S.A theory of adaptive pattern classifiers [J].IEEE Trans.Electronic Computers,1967,16:299-307.
  • 2[2]Amari S.Natural gradient works efficiently in learning [J].Neural Comoutation,1998,10:251-276.
  • 3[3]Amari S,Cichocki A.Adaptive blind signal processing:Neural network approaches [J].Proc.IEEE,1998 ,86:2026-2048.
  • 4[4]Basak J,Amari S.Blind separation of uniformly distributed signals:A general approach [J].IEEE Trans.Neural Networks,1999,10:l173-1185.
  • 5[5]Bell A J,Sejnowski T J.An information-maximization approach to blind separation and blind deconvolution [J].Neural Computation,1995,7:1129-1159.
  • 6[6]Burel G.Blind separation of .sources:A nonlinear neural algorithm [J].Neural Networks,1992,5:937-947.
  • 7[7]Cao X R,Liu R W.A general approach to blind source separation [J].IEEE Trans.Signal Processing,1996,44:562-571.
  • 8[8]Cardoso J F.Blind signal separation:Statistical principles [J].Proc.IEEE,1998,86(10):2009-2025.
  • 9[9]Cardoso J F,Laheld B.Equivariant adaptive source separation [J].IEEE Trans.Signal Processing,1996,44:3017 - 3029.
  • 10[10]Cardoso J F,Souloumiac A.Blind beamfomrming for non-Gaussian signals[J].lEE Proc.-F,1993,140:362-370.

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