对人脸图象作正交小波变换，得到它在不同频带上的四个子图象，然后分别提取奇异值特征。
We do orthogonal wavelet transform of a face image, get its four sub images of different frequency bands, then respectively extract their singular value features.
重点介绍了图像的代数特征抽取以及作者关于奇异值特征矢量作为图像的一种代数特征方面的研究工作。
The algebraic feature extraction of images and the authors' research work on singular value vectors as a kind of algebraic features of images are emphasized.
通过矩阵运算，证明了奇异值特征矢量在图像做缩放变换时具有不变性，并将这种性质运用于图像匹配。
By matrix operation, the invariance of image singular value vector about the reducing and enlarging transformation is proved, and this character is applied to the image matching.
本文主要针对以奇异值特征为代表的代数特征方法，在3d目标识别及姿态估计等方面做了大量研究工作。
This thesis mainly aiming at the method of algebraic character that is represented by SVD has done plenty of research work in the aspects of 3d object recognition and pose estimation.
传统的特征提取方法主要有：主分量分析、奇异值分解、投影追踪、自组织映射等。
Classical feature extraction methods include: Principle Component Analysis, Singular Value Decomposition, Projection Pursuit, Self-Organizing Map, and so on.
奇异值是矩阵的一个良好特征。
在训练阶段，从人脸图像集中选取大量人脸图像的信息矩阵的奇异值向量作为矩阵中的一列而构成的人脸图像集特征矩阵。
In training phase, singular value vector of a lot of face image information matrix from face image sets constitutes face image sets characteristic matrix as a column.
传统的模极大值序列处理方法虽然可以保留信号特征，但降噪后的信号在奇异点有毛刺和轻微的振荡。
Although traditional processing method of modulus maximum array can retain characteristics of signal, the signal after de-noising exists thorns and slight oscillation at singularity point.
提出了基于小波包变换和改进奇异值分解的高分辨雷达目标一维距离像特征提取方法。
A feature extraction method of high-range-resolution radar profiles, which takes advantage of wavelet packet transform and modified SVD (singular value decomposition) was proposed.
其中人脸特征提取采用了奇异值分解和主分量分析法，身份验证则采用了以类内平均距离为判据的算法。
Here, we use the singular value decomposition and principal component analysis for facial feature extraction, using the average distance category as discrimination on the basis of authentication.
利用奇异值理论提取包含矩阵的奇异值作为气缸爆发噪声信号的特征。
Singular value is extracted as to feature of cylinder explosive noise signal from Cover Matrix with singular value theory.
然后对人脸图像做奇异值分解和离散傅立叶变换，并分别提取最佳鉴别变换特征，用最近邻方法进行分类。
The optimal discriminant features of face are extracted using singular value decomposition and Fourier transform, and then they are classified by the nearest neighbor method. In...
首先基于差分图像和肤色信息检测出人脸，其次使用改进的奇异值分解方法提取面部特征，最后运用最小距离分类器进行识别。
First based on the difference image and the skin color the face is detected and localized, then features are extracted by using improved SVD, last the features is classified by minimal distance.
引进了拟块有向边覆盖对角占优矩阵概念，给出了新的矩阵非奇异判定定理和特征值分布定理。
We introduced the concept of block directed edge cover diagonal quasi dominant matrix, obtained a new nonsingularity criteria for matrices and distribution theorem on eigenvalues of matrix.
每一幅图像都可以认为是一个矩阵，而矩阵的奇异值具有很多适合图像识别的特征。
Any image can be considered as a matrix. Singular values (SV) of the matrix have some important properties adaptable to image recognition.
对于非奇异单圈混合图，范益政给出了其最小特征值所对应特征向量的一个很好结构性质。
In the case of being a unicyclic mixed graph, Fan Yi-Zheng gives a remarkable result on the structure of the eigenvectors corresponding to its smallest eigenvalue.
子图像的特征奇异值组成整个图像的局部奇异值向量，作为分类器的输入。
With the data of characteristic singular values of sub-images, the Local Singular Value Vector of the whole image are combined, and are used as the input of the classifier.
利用奇异值分解技术，构造该频带内一组与频率无关的特征模正交基。
The antenna characteristics can be determined rapidly by the frequency independent basis functions.
对应特征点的三维重建是根据三角测量的方法计算其投影矩阵，然后用奇异值分解求出特征点的三维齐次坐标。
Feature points' 3d coordinates are computed through singular value decomposition of projector matrix, then compute projector matrix by triangulation.
讨论了矩阵乘积ab与BA的特征值、特征向量及秩等的关系，并得到了矩阵的奇异值分解。
The relationship between the eigenvalue, eigenvector and the rank of the product of matrices AB and BA is discussed, and the factorization of the singular values of matrix a is obtained.
作为多元凸函数的控制不等式在矩阵方面的初步应用，获得了矩阵特征值和奇异值的简洁的控制不等式。
As applications of majorizing inequality of convex function of several variables, some majorizing inequalities of eigenvalue and singular value of matrix are obtained.
该文提出了基于奇异值分解的个性化信息匹配算法，可以利用用户的特征信息完成个性化的信息匹配。
This paper puts forward SVD algorithm to realize personal information retrieval, and verifies the effectiveness of the algorithm.
该方法首先对地球物理和地球化学等网格数据进行二维矩阵的奇异值分解，之后用左特征向量矩阵与右特征向量矩阵的直积构造一个正交完备基。
The MSVD method constructs a self-contained orthogonal basis using the outer product of left and right eigenvector matrixes decomposed from 2D geochemical or geophysical maps.
该方法首先对地球物理和地球化学等网格数据进行二维矩阵的奇异值分解，之后用左特征向量矩阵与右特征向量矩阵的直积构造一个正交完备基。
The MSVD method constructs a self-contained orthogonal basis using the outer product of left and right eigenvector matrixes decomposed from 2D geochemical or geophysical maps.
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