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local feature analysis
相关语句
  局部特征分析
     Studies on the Local Feature Analysis Orientated Sculpt Design Methods of Product
     基于产品局部特征分析的造型设计方法
短句来源
     The product local feature analysis algorithm has good reconstruction ability and stresses the product local feature. It is a new reconstruction method for the product reverse design and gives the certification of the product local feature design.
     产品局部特征分析的新算法,具有良好的全局重构能力,重视产品的局部特征,为产品局部特征分析提供了更深更广意义的理解,为产品设计反求工程提供一种新的形态重构方法,为产品局部特征深入设计提供依据。
短句来源
     Face Recognition Based on Local Feature Analysis
     基于局部特征分析的人脸识别方法
短句来源
     Discriminant local feature analysis with applications to face recognition
     鉴别局部特征分析及其在人脸识别中的应用
短句来源
     This paper proposes discriminant local feature analysis (DLFA) algorithm which uses local feature analysis (LFA) instead of PCA before the LDA.
     该算法中,局部特征分析(LFA)代替PCA作为线性鉴别分析(LDA)的前端。
短句来源
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  “local feature analysis”译为未确定词的双语例句
     In order to improve precision, we refer to the idea of LFA (Local Feature Analysis) and give a model of ML-IDAM (ICA-based Multi-Layer Directly Appearance Model), which can give more precise experiment results.
     为了提高算法精度,我们还借鉴LFA的思想,提出了一个基于局部特征的分层学习、匹配模型ML-IDAM,得到了更精确的实验结果。
短句来源
  相似匹配句对
     Feature:
     本文特色:
短句来源
     The Local Feature and Modern City Innovation
     地域特征与现代城市更新
短句来源
     The Construction of Local Feature and Journal Character
     地方特色与学报特色构建
短句来源
     Face Recognition Based on Local Feature Analysis
     基于局部特征分析的人脸识别方法
短句来源
     Conspicuous Feature Analysis of Local Regions in an Image
     图象局部突出特征分析
短句来源
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  local feature analysis
Four families of global features are considered: geometric moments, eigenfeatures, Local Feature Analysis vectors, and a novel feature called Pose-Image Covariance vectors.
      
The information extracted through global and local feature analysis is stored in the contextual XTM documents.
      


Principal component analysis (PCA) was performed before linear discriminant analysis (LDA) in the traditional discriminant karhunen loeve (DKL) algorithm. However, principal component analysis is based on global information, which ignores significant local characteristics. This paper proposes discriminant local feature analysis (DLFA) algorithm which uses local feature analysis (LFA) instead of PCA before the LDA. LFA captures the local characteristics with little loss of global information...

Principal component analysis (PCA) was performed before linear discriminant analysis (LDA) in the traditional discriminant karhunen loeve (DKL) algorithm. However, principal component analysis is based on global information, which ignores significant local characteristics. This paper proposes discriminant local feature analysis (DLFA) algorithm which uses local feature analysis (LFA) instead of PCA before the LDA. LFA captures the local characteristics with little loss of global information and it provides a low-dimensional representation of the signals, which reduces the dimensionality for LDA. The new algorithm was compared with the DKL algorithm on three databases for open-set face verification. The tests demonstrated that by combining LFA and LDA, the DLFA algorithm outperforms the DKL algorithm, reducing the error rate by 43.10% on the PoliceFace database, by 25.87% on the OCRLab database and by 33.16% on the combined database.

由于传统的鉴别主分量分析(DKL)算法中,主分量分析(PCA)基于全局特征,难以提取人脸的局部特性,该文提出鉴别局部特征分析算法。该算法中,局部特征分析(LFA)代替PCA作为线性鉴别分析(LDA)的前端。一方面,LFA在保留大部分全局信息的同时提取局部特征。另一方面,它为信号提供一种有效的低维表示,增强LDA在小样本问题中的数值稳定和推广性能。文中结合开集模式的人脸认证领域,在PoliceFace、OCRLab人脸库和它们的组合库上对新算法和DKL算法进行实验比较。实验表明,通过结合LFA和LDA,新算法明显降低认证错误率:在PoliceFace库上,等错误点错误率降低43.10%;在OCRLab库上错误率降低25.87%;在组合库上错误率降低33.16%。

On the basis of the analysis of traditional elastic graph matching, a face recognition algorithm based on local feature analysis and optimization matching is proposed. Firstly, some important face features are located by means of neutral network. Secondly, the multiscale features of the feature points are extracted with the local mutiscale analysis feature of the Gabor wavelet. In this way, every face feature point is represented by a series of Gabor wavelet coefficients....

On the basis of the analysis of traditional elastic graph matching, a face recognition algorithm based on local feature analysis and optimization matching is proposed. Firstly, some important face features are located by means of neutral network. Secondly, the multiscale features of the feature points are extracted with the local mutiscale analysis feature of the Gabor wavelet. In this way, every face feature point is represented by a series of Gabor wavelet coefficients. Finally, in order to find the face needed, the test face is compared with the multiscale features of the corresponding feature points in the face database with the optimization matching. Here the optimization matching method is proved strictly. The test results about Yale and ORL face database show that not only the proposed method is far better than the traditional EigenFace method but also the effect of the illumination variation on the face recognition is obviously overcome and the method has quite good robust for face expression variation in some degree.

在分析传统弹性图匹配的基础上,提出一种基于局部特征分析(LFA)与最优化匹配的人脸识别算法.该算法首先利用神经网络方法估计出在识别人脸中起重要作用的一些特征点(如瞳孔、眼角、眉心、眉角、嘴角等),之后利用Gabor小波的局部多尺度分析特性提取特征点的多尺度特征.这样人脸的每个特征点就被一系列的Gabor小波系数所表示,最后对待识人脸与人脸库中人脸的相应特征点的多尺度特征进行最优化匹配找出需要的人脸.对最优化匹配方法给出了严格的数学证明,同时也给出了Yale大学和ORL人脸库上的测试结果.理论和实验证明,该方法远优于传统的EigenFace方法,同时能有效地克服光照变化对人脸识别的影响,在一定程度上对表情的变化也有较好的鲁棒性.

A new algorithm based on local feature analysis of product has been investigated.It has good reconstruction ability and stresses the local feature of product.It is a new reconstruction method for the reverse design of product and gives the gist for the thorough design of the local feature of product.

探讨了产品局部特征分析的新算法,具有良好的全局重构能力,重视产品的局部特征,为产品局部特征分析提供了更深更广意义的理解,为产品设计反求工程提供一种新的形态重构方法,为产品局部造型深入设计提供依据.

 
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