查询词典 feature vector

In accordance with the actual project, this paper fully considers the comprehensive factors of speaker recognition system on the basis of explaining speaker recognition's principle, such as recognition rate, recognition speed, system stability etc. LPCC is used as the feature parameter. LPCC can reflect the esthesia feature of human in pronunciation and has offered a group of succinct speech signal model parameters which signify the range of frequency spectrum of the speech signal more accurately and the operation amount is not comparatively big. Combined vector quantization technology, the speech searched system has been developed.

本文依拖实际项目，在阐述说话人识别原理的基础上，充分考虑说话人识别系统的识别率、识别速度、稳定性等综合因素，采用能够反映人对语音的感知特性的线性预测倒谱系数LPCC 作为特征参数，LPCC 的重要性还在于，它提供了一组简洁的语音信号模型参数，这一组参数较精确地表征了语音信号的频谱幅度，而分析它们所需的运算量相对来讲却不大；同时结合矢量量化技术，成功开发出语音检索系统。

First we construct a covariance matrix from sample images, then compute the eigenvalues and corresponding eigenvectors of the covariance matrix, construct a feature matrix with the eigenvectors. Then every images in database can be projected into the feature matrix and gain a projection vector, so does the input image. Then we can judge the resemblance between input image with each image in database by computing the distance between their projection vectors.

我们首先根据采集的样本图像构造一个协方差矩阵，然后求取该矩阵的特征值，以这些矩阵特征值对应的特征向量构造出一个特征空间，然后将输入图像向该特征空间映射，将获取的映射系数与样本库中图像的映射系数进行距离计算，根据计算出的距离判定输入图像与样本图像间的匹配程度。

The design of vehicle type classification based on Support Vector Machine is collecting characteristic parameters provided to segregator after vehicle image preprocessing and feature extraction,and classifying the type of vehicle by using technique of Support Vector Machine and binary decision tree according to vehicle image from video camera.

基于支持向量机的车型分类的设计思路是通过视频采集获得车辆图像，对车辆图像进行图像预处理和特征提取后，得到分类器所需数据特征，而后采用支持向量机和二元决策树对车型分类。

The algebraic and geometric invariance of singular value vector is firstly given,and then singular value vector compressed is extracted as the image feature,then RBF neural network is used to training and recognition.

首先说明了彩色图像的奇异值向量具有代数和几何不变性，再将降维的奇异值向量作为图像的特征，然后应用RBF神经网络进行训练和识别。

In this algorithm, the strongest corner points is adaptively detected with Harris operator and (2n+1)×(2n+1) feature windows is selected for originally mutual gray correlative matching. Continental distance ratio invariant criterion is proposed, and consistency of motion vector is improved by eliminating error matching points with median filter. 9 pairs right points is preserved, 4 pairs composing 1 group, and 5 groups is random distributed to uncoil the motion vector equations. Last, stabilization precision is improved by averaging the 5 groups' solutions.

该方法用Harris算子自适应检测出最强壮的角点，开辟(2n+1)×(2n+1)特征窗进行灰度互相关初匹配；提出欧式距离之比不变准则，通过中值滤波剔除误匹配点，确保运动矢量较高的一致性；最后保留若干正确的匹配点对，分成多组解运动矢量方程，将多组解的平均值作为全局运动矢量以提高稳像精度。

This approach first aligned 3D faces based on non-uniform mesh re-sampling which result the uniform number of vertex and topology structure and kept in a vector. Second, we changed the vector to the matrix by division and overlap, and then 2DLDA suggests a feature selection strategy to select the most discriminative features from the corner.

该算法首先使用基于非均匀网格重采样的方法对所有三维人脸做规格化处理，使三维人脸具有统一的点数和拓扑结构；其次，以先分段、再重叠的形式将原本一维向量表示组织为二维矩阵表示，然后使用二维线性判别分析方法(2DLDA)对获得的数据进行特征抽取。

The simulation results show that the model based on potential support vector machine not only has much stronger generalization ability but also has the ability of feature selection which is not owned by other ones based on standard support vector machine.

数据仿真显示该模型同标准支持向量机模型相比不仅具有更好的推广能力，而且具备较好的特征变量自动选取功能。

Based on the statistic feature of block matching moving vector, the method calculates the basic translation vector between current frame and referenced frame through a revisable parameter, and adaptively chooses processing algorithm for image sources with different characteristics.

该方法从分析块匹配运动矢量统计特性出发，通过引入一个可预设置参数，计算当前帧与参考帧之间的基本平移矢量，并自适应地采取混合全局运动、局部运动估计的算法处理不同特征的源数据。

The method firstly organizes support vectors in clusters in feature space, and then,it finds the pre-images of the cluster centroids in feature space to construct a reduced vector set.

此方法首先在特征空间中对支持向量进行聚类，然后寻找特征空间中的聚类中心在输入空间中的原像以形成约简向量集。

The whole process of text representation were discussed — word segmentation, building stop words list, feature selection, weight computation and generating vector space. Aim at the influence of stop words, a list, which is fit for text categorization, is set up and makes the vector dimension reduce.

针对停用词对分类的影响，建立了适合文本分类的停用词表，使向量维数得到初步降低；对现有特征选择方法进行了介绍和对比，构造了一种适合支持向量机的特征选择函数——基于类内频率的特征选择函数。

Chrysanthemum of 10 thousand birthday is lax to edaphic requirement, with the arenaceous qualitative loam with fecund, good drainage had better.

万寿菊对土壤要求不严，以肥沃。排水良好的砂质壤土为好。

He unstepped the mast and furled the sail and tied it.

他拔下桅杆，把帆卷起，系住。