Description: 现有的代数特征的抽取方法绝大多数采用一维的方法，即首先将图像转换为一维向量，再用主分量分析（PCA）,Fisher线性鉴别分析（LDA）,Fisherfaces式核主分量分析（KPCA）等方法抽取特征，然后用适合的分类器分类。针对一维方法维数过高，计算量大，协方差矩阵常常是奇异矩阵等不足，提出了二维的图像特征抽取方法，计算量小，协方差矩阵一般是可逆的，且识别率较高。-existing algebra feature extraction method using a majority of the peacekeepers, First images will be converted into one-dimensional vector, and then principal component analysis (PCA), Fisher Linear Discriminant Analysis (LDA), Fisherfaces audits principal component analysis (KPCA), and other selected characteristics, then use the appropriate classification for classification. Victoria against an excessive dimension method, calculation, covariance matrix is often inadequate singular matrix, a two-dimensional image feature extraction method, a small amount of covariance matrix is usually reversible, and the recognition rate higher. Platform: |
Size: 2048 |
Author:小弟 |
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Description: 主分量分析(PCA ) 是统计学中分析数据的一种有效的方法, 可以将数据从高维数据空间变换到低维特征空间, 因而
可以用于数据的特征提取及压缩等方面。在该文的形状识别系统中, 用PCA 法提取图像的形状特征, 能够较好地满足识别
层的输入要求。在识别层研究了3 种识别方法: 最近邻法则、BP 网络及协同神经网络方法, 均取得了满意的实验效果。-Principal component analysis (PCA) is a statistical analysis of data in an effective method to data from high dimensional data space transformation to the low-dimensional feature space, which can be used for data feature extraction and compression and so on. In this paper, the shape recognition system using PCA extraction of the shape of image features, can be used to satisfy the identification requirements of the input layer. In the recognition layer studied three kinds of identification methods: nearest neighbor rule, BP network and the synergetic neural network methods, have achieved a satisfactory experiment results. Platform: |
Size: 277504 |
Author:陈平 |
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Description: 该程序包实现了模式识别中的两个特征提取算法，主成分分析PCA和线性判别分析LDA。采用C++语言编写，开发环境VS。 程序包还提供了两个测试样本文件。-The package to achieve the recognition of the two feature extraction algorithm, principal component analysis PCA and linear discriminant analysis LDA. Using C++ language, development environment VS. Package also provides two test samples. Platform: |
Size: 46080 |
Author:tangxiaojun |
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Description: 用MATLAB实现了基于pca的特征提取方法，有效地完成了识别，是识别的经典例子-Pca-based implementation using MATLAB feature extraction methods, efficient completion of the identification is to identify the classic example of Platform: |
Size: 1024 |
Author:黄静 |
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Description: Using C + + language of the PCA-SIFT algorithm, can be used for visual odometer feature extraction and matching results with good match Platform: |
Size: 61440 |
Author:jason li |
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Description: pca特征向量提取 利用pca的方法获取特征植及特征向量 最后可以自己根据需要降维-pca pca feature vector extraction method using characteristics of plants and to obtain the final feature vector dimension reduction can be their own as needed Platform: |
Size: 4096 |
Author:张天号 |
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Description: 用pca方法提取特征脸，可调参数，每次选择20张脸，或者分行显示-Face feature extraction using pca methods, adjustable parameters, 20 face each selection, or branches show Platform: |
Size: 4096 |
Author:翟磊 |
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Description: 采用PCA对人脸特征进行抽取，用SVM多累分类器对人脸进行识别，有操作界面-Using PCA for facial feature extraction, and more tired with the SVM classifier for face identification, a user interface Platform: |
Size: 9036800 |
Author:钟佩 |
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Description: 利用PCA提取人脸特征，。利用PCA技术降低维数去除了原始原始数据之间的关联性。-Using PCA face feature extraction,. Using PCA technology to reduce dimension in addition to the original connection between the original data. Platform: |
Size: 2048 |
Author:安占福 |
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Description: 人脸检测一直是人们在研究的问题，流形学习用于人脸检测中的特征提取，用PCA与constructM进行降维，KNN分类器用于分类。取得非常好的效果。-Face detection has been the problem of people in the study, manifold learning for face detection feature extraction using PCA and constructM dimension reduction, KNN classifier for classification. Achieved very good results. Platform: |
Size: 980992 |
Author:abcpei |
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Description: MATLAB编程，提取LFP功率特征，采用PCA降维，实现不同颜色数据分类-MATLAB programming, LFP power feature extraction using PCA dimensionality reduction to achieve different color data classification Platform: |
Size: 2048 |
Author:mayanbo |
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Description: 本程序实现人脸对齐功能，它通过提取人脸特征，利用PCA降维，实现人脸对齐效果-This program implements the face alignment feature, it is through facial feature extraction using PCA dimensionality reduction to achieve the effect of face-aligned Platform: |
Size: 4096 |
Author:云长 |
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