提出了一种新的目标轮廓特征级融合方法，求解两类模式图像的收敛动态轮廓线控制点向量差的范数平方极小化。
A new feature level fusion method of target's contour was proposed, which minimizes norm's square of the difference of control point vectors of convergent dynamic contours in two modal images.
解决了应用SVM识别算法对遥感矿化信息提取过程中输入样本特征向量（微弱信息样本）的构造问题。
Through SVM algorithm, solving the building problem of input sample feature vector (weak information sample) in the process of extracting mineralizing information from RS data.
并对该特征向量进行对数归一化，将归一化的特征向量作为径向基函数（RBF）神经网络的输入，在此基础上进行识别，达到较好的识别效果。
The normalized vector is used as the input of RBF NN, and target recognition is performed based on this, which leads to a satisfactory recognition result.
提出了一种基于特征向量展开的均匀化方法，用以求解正交编织复合材料的宏观力学性质。
A homogenization method based on the eigenvector expansions is developed to evaluate the macro mechanical properties of orthogonal woven fabric composites in the present paper.
该方法给出了该类观测器的增益矩阵和左特征向量矩阵的参数化表达式。
This method presents the parametric expressions for the gain matrices and the left eigenvector matrix of the high-order PI observers.
用统计学的方法，等角度间隔地计算归一化步态轮廓图像各像素点至质心距离的均值与方差，并用其构造步态识别的特征向量。
Using statistical methods to calculate the Angle interval mean and variance of pixels to centroid distance of the normalized gait silhouette images, and construct them as a feature vector.
该方法包括以下几个步骤：（1）先将训练样本归一化，再利用PCA变换进行降维，获得样本特征向量；
The proposed method is composed of the following three parts:(1) The feature space is reduced by the PCA(the principal component analysis) on the normalized input spectra;
然后对二值化的图像进行特征提取，获得疵病的数字化信息和特征向量。
Then implement binary image feature extraction to acquire digital information and eigenvectors.
然后对二值化的图像进行特征提取，获得疵病的数字化信息和特征向量。
Then implement binary image feature extraction to acquire digital information and eigenvectors.
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