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1)  two directional two dimensional principal component analysis((2D)2 PCA)
行列相结合的二维主成分分析
1.
Following these,feature reduction was effected using two directional two dimensional principal component analysis((2D)2 PCA) and column directional two dimensional principal component analysis(2DPCA) respectively.
在周期分割后的特征提取阶段分别使用GEI结合行列相结合的二维主成分分析((2D)2PCA)方法和对步态序列图像进行Radon变换,在周期模板构造后用列方向的二维主成分分析(2DPCA)降维方法进行数据压缩。
2)  (2D)~2PCA
行列相结合的2维主成分分析
1.
Finally, (2D)~2PCA and W(2D)~2PCA is used to reduce into a low dimension space.
该方法首先应用背景减除法分割出人体轮廓;然后通过人体宽高比的相关信号确定运动周期,再对二值周期序列进行步态能量图像(GEI)合成;最后运用行列相结合的2维主成分分析((2D)~2PCA)方法与加权的2维主成分分析(W(2D)~2PCA)方法提取特征主向量,并采用最近邻分类器进行分类。
3)  two-dimensional principal component analysis
二维主成分分析
1.
Then the two-dimensional principal component analysis approach is applied to the training images represented by ROIs to get the statistical feature space.
该算法根据奇异点的位置和方向,提取指纹图像的感兴趣区域(ROI),并使用二维主成分分析(2DPCA)的方法进行统计特征的提取和识别。
2.
This paper proposes face recognition software that uses two-dimensional principal component analysis (2DPCA) in conjunction with partial feature weighting by applying two-dimensional partial-weighting to the characteristic subspace.
提出了一种将局部特征加权与二维主成分分析相结合的局部加权的二维主成分分析方法。
3.
Based on two-dimensional principal component analysis,this paper investigates the features of manifold distribution.
在二维主成分分析的基础上,考虑样本的流形分布特点,引入样本相似系数,重新定义了样本拉普拉斯散布矩阵,进而给出了基于拉普拉斯二维主成分分析的特征提取方法。
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4)  2DPCA
二维主成分分析
1.
A feature extraction method for palmprint recognition based on Two-Dimensional Principal Component Analysis(2DPCA)is proposed in this paper.
论文提出了将二维主成分分析方法(2DPCA)应用于掌纹识别的特征提取,并在PolyU掌纹数据库上利用最近邻分类器与余弦距离度量进行了相应的实验,得到了99。
2.
From researching on the universal principle of feature fusion of image,a new algorithm was proposed which based on the 2 dimension principal component analyses(for short 2DPCA).
通过对图像特征融合的一般规律的研究,提出了一种基于二维主成分分析(简称2DPCA)的图像特征融合算法。
3.
Based on the theory of statistics, this dissertation investigates two aspects of unsupervised method: (a) the systematical study of some topics that arise in finite mixtures of models, and (b) the researches on nonlinear extensions to two-dimensional principal component analysis (2DPCA), during which we take face recognition into account.
本文以统计理论为基础,研究两个方面的内容:(a)对有限混合模型的有关议题进行了较为系统的研究;(b)结合人脸识别问题,研究了二维主成分分析的非线性扩展。
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5)  Two Dimensional Principal Component Analysis(2DPCA)
二维主成分分析
1.
Two Dimensional Principal Component Analysis(2DPCA) extracts the global feature of human face,but the local feature is very important to face recognition.
针对二维主成分分析(2DPCA)提取的是人脸的全局特征,但局部特征对人脸识别的作用非常大,提出了一种基于局部特征的自适应加权2DPCA。
2.
Based on Two Dimensional Principal Component Analysis(2DPCA),a new technique called Modular Two Dimensional Principal Component Analysis(M2DPCA) is developed for human face recognition in this paper.
基于二维主成分分析(2DPCA),文章提出了分块二维主成分分析(M2DPCA)人脸识别方法。
3.
Following these,feature reduction was effected using two directional two dimensional principal component analysis((2D)2 PCA) and column directional two dimensional principal component analysis(2DPCA) respectively.
在周期分割后的特征提取阶段分别使用GEI结合行列相结合的二维主成分分析((2D)2PCA)方法和对步态序列图像进行Radon变换,在周期模板构造后用列方向的二维主成分分析(2DPCA)降维方法进行数据压缩。
更多例句>>
6)  Modular Two Dimensional Principal Component Analysis(M2DPCA)
分块二维主成分分析
1.
Based on Two Dimensional Principal Component Analysis(2DPCA),a new technique called Modular Two Dimensional Principal Component Analysis(M2DPCA) is developed for human face recognition in this paper.
基于二维主成分分析(2DPCA),文章提出了分块二维主成分分析(M2DPCA)人脸识别方法。
补充资料：行列
人或物排成的直行和横行的总称：他站在～的最前面◇这家工厂经过整顿，已经进入了同类企业的先进～。
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