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1)  independent component analysis(ICA)
独立组分分析
1.
First,the Vis/NIR loading weight of rough rice with different years was got by using independent component analysis(ICA)and setting the wavelengths corresponding to the maximal correlation as the inputs of artificial neural network(ANN),then the discrimination model was build.
建立了一种基于独立组分分析的可见/近红外光谱反射技术快速鉴别稻谷年份的新方法。
2.
Independent component analysis(ICA) was put forwarded to select several optimal wav.
提出了一种基于独立组分分析的可见/近红外光谱透射技术快速鉴别蜂蜜品牌的新方法。
2)  independent component analysis
独立组分分析
1.
Application of Independent Component Analysis to the IR spectra Analysis;
独立组分分析在红外光谱分析中的应用
2.
A new method is presented to separate the IR spectrum based on the independent component analysis (ICA) .
独立组分分析(Independent Component Analysis,ICA)应用于混合红外光谱定性分析。
3)  independent constituent
独立组分
4)  Independent component analysis
独立元分析
1.
On-line monitoring of gas metal arc welding defects based on independent component analysis;
基于独立元分析的GMAW缺陷在线监测
2.
Contorted objects recognition based on independent component analysis;
基于独立元分析的扭曲目标识别
3.
Analysis of Electrocardiogram Signals Based on Independent Component Analysis;
基于独立元分析的心电信号分析
5)  independent component analysis(ICA)
独立元分析
1.
A new denoising method is presented in the paper, based on the independent component analysis(ICA) and the noise independent component selection measurement which is the dispersivity of the independent component's projection coefficients to each electrode.
使用独立元分析方法,提出了一种以独立元对各电极点投影系数的离散度为噪声独立元选取准则,设计了一套心外膜标测电位去噪新方法。
6)  ICA
独立元分析
1.
A New Scheme for Affine Invariant-Descriptor and Affine Transformation Parameter Estimation Based on ICA;
一种基于独立元分析的仿射不变描述和仿射变换参数估计的新方法
2.
In face recognition traditional Independent Component Analysis (ICA) is to convert face image matrix into vector to find whitened matrix, and separate matrix is solved by way of Fast ICA.
传统独立元分析(Independent Component Analysis,ICA)用于人脸识别首先是将人脸图像矩阵转换成向量求白化矩阵,然后利用快速固定点算法求分离矩阵,获得人脸图像独立基子空间,从而实现人脸识别。
3.
General Face Animation Mode Expression Based on ICA;
然后通过对获取数据应用独立元分析获得一般人脸动画模式,最终使用ICA参数空间生成任意特定人的面部表情。
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