Description: 基于pca降维的特征不变特性变换方法（sift），希望对研究特征提取的朋友有帮助！-PCA dimensionality reduction based on the characteristics of the same characteristics of transformation method (sift), hope that the study of feature extraction has to help a friend! Platform: |
Size: 4120576 |
Author:孙艳丽 |
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Description: 提出一种新的非线性变换的彩色空间 ″″, 利用次高斯概率分布函数拟合皮肤色度信息, 得到候选区
YC C
r b
域。为了排除候选区域中的非人脸, 首先根据均值和方差信息分割出候选区域中的纹理特征信息, 再通过多尺度
)
(
信息定位眼睛, 然后根据人脸特征的几
形态边缘检测算子检测候选区域的边缘, 利用 边缘方向
PCA PCAED
( )
何形状信息定位其他特征 鼻、嘴 , 通过这些几何特征信息对肤色分割得到的候选区域进行验证, 最终得到正确
的人脸区域。利用3 个实验数据集测试该算法, 并与其它相应的算法相比较, 提出的非线性彩色空间对于肤色分
割具有很好的效果, 且对光照和姿态具有良好的不变性。另外, 利用 信息和几何特征信息检测人脸特征
PCAED
具有很高的定位精度, 定位检测率优于其他方法。实验结果表明, 该算法具有定位准确率高, 漏检率和误检率低
等特点。-
A novel approach for skin segmentation and facial feature extraction is proposed
The proposed skin segmentation is a method for integrating the chrominance components of
″″ . ″″
r b r b
nonlinear YC C color model The chrominance components of nonlinear YC C color space
,
are modeled using a subgaussian probability density function and then the face skin is seg
. ,
mented based on this function In order to authenticate the face candidate regions firstly tex
ture information in face candidate regions is segmented using mean and variance of luminance
, . ,
information and then the eye is located by the PCA edge direction information Finally the
, ,
others features such as nose and mouth also are detected using the geometrical shape infor
. 2 ,
mation As all the above mentioned techniques are simple and efficient the skin segmentation
.
based on nonlinear color spacemethod has the invariability of lighting and pose In the experi
, .
ments themethod has been successfull Platform: |
Size: 458752 |
Author:zz |
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Description:
在特征提取阶段，研究了PCA, 2DPCA, (2D) 2PCA, DiagPCA, DiagPCA-F-2DPCA等多
种方法。不同于基于图象向量的PCA特征提取，由于2DPCA, (2D) ZPCA, DiagPCA和
DiagPCA-I-2DPCA的特征提取都直接基于图象矩阵，计算量小，所以特征的提取速度明
显高于PCA方法。-In the feature extraction stage, the study of the PCA, 2DPCA, (2D) 2PCA, DiagPCA, DiagPCA-F-2DPCA and other methods. Vector is different from the PCA-based image feature extraction, as 2DPCA, (2D) ZPCA, DiagPCA and DiagPCA-I-2DPCA the feature extraction are directly based on image matrix, a small amount of calculation, so the speed of feature extraction method was significantly higher than PCA . Platform: |
Size: 45056 |
Author:付采 |
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Description: This paper identifies a novel feature space to
address the problem of human face recognition from
still images. This based on the PCA space of the
features extracted by a new multiresolution analysis
tool called Fast Discrete Curvelet Transform. Curvelet
Transform has better directional and edge
representation abilities than widely used wavelet
transform. Inspired by these attractive attributes of
curvelets, we introduce the idea of decomposing
images into its curvelet subbands and applying PCA
(Principal Component Analysis) on the selected
subbands in order to create a representative feature
set. Experiments have been designed for both single
and multiple training images per subject. A
comparative study with wavelet-based and traditional
PCA techniques is also presented. High accuracy rate
achieved by the proposed method for two well-known
databases indicates the potential of this curvelet based
feature extraction method.-This paper identifies a novel feature space to
address the problem of human face recognition from
still images. This is based on the PCA space of the
features extracted by a new multiresolution analysis
tool called Fast Discrete Curvelet Transform. Curvelet
Transform has better directional and edge
representation abilities than widely used wavelet
transform. Inspired by these attractive attributes of
curvelets, we introduce the idea of decomposing
images into its curvelet subbands and applying PCA
(Principal Component Analysis) on the selected
subbands in order to create a representative feature
set. Experiments have been designed for both single
and multiple training images per subject. A
comparative study with wavelet-based and traditional
PCA techniques is also presented. High accuracy rate
achieved by the proposed method for two well-known
databases indicates the potential of this curvelet based
feature extraction method. Platform: |
Size: 432128 |
Author:Swati |
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Description: 提出一种基于主分量分析和相融性度量的快速聚类方法。通过构造主分量空间将高维数据投影到两个主成分上
进行特征提取，每一个主分量都是原始变量的线性组合-Is proposed based on Principal Component Analysis and Measure of blending fast clustering method. Principal component space by constructing a high-dimensional data onto two principal component on feature extraction, each principal component is a linear combination of original variables Platform: |
Size: 144384 |
Author:f0700 |
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Description: 线性判别分析(LDA)是一种较为普遍的用于特征提取的线性分类方法。但是将LDA直接用于人脸识别
会遇到维数问题和“小样本”问题。人们经过研究，通过多种途径解决了这两个问题并实现了基于I,DA的人脸识
别 文章对几种基于LDA的人脸识别方法做了理论上的比较和实验数据的支持，这些方法包括Eigenfaces、Fish—
erfaceS、DLDA、VDLDA及VDFLDA。实验结果表明VDFLDA是其中最好的一种方法。-Low—dimensional feature representation with enhanced discriminatory power is of paramount importance to face
recognition(FR)system．Linear Discriminant Analysis(LDA)is one of the most popular linear classification techniques of
feature extraction。but it will meet two problems as computational challenging and “small sample size’’when applying to
face recognition directly．After studying people solve the two problems through several ways and realize the face recogni—
tion based on LDA． The short paper here makes compare on theory and experimental data analysis on several Face
Recognition system using LDA—Based Algorithm，such as Eigenfaces(using PCA)，Fisherfaces，DLDA，VDLDA and VD—
FLDA．The experimental results show that the VDFLDA method is the best of al1． Platform: |
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Author:费富里 |
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Description: 人脸识别技术是计算机模式识别领域非常活跃的研究课题，在法律、商业等领域有
着广泛的应用前景。自动人脸识别系统一般由两个模块组成：定位与检测模块，特征提
取与识别模块。本文对两个子模块进行了详细讨论，通过实验仿真了一个基于静态图像
的人脸识别系统。为提高系统的识别率，本文对定位检测模块和特征提取模块进行了深
入研究。
针对复杂多变人脸检测和定位问题，实现了一种基于对称特征的人脸定位方法。该
算法首先基于肽色特征提取出人脸区域，根据眼睛的颜色和梯度特征在肤色区找到眼睛
可能存在的有限的候选区域：根据人脸的对称特征，把这些候选区域分别进行匹配，找
到最佳匹配的区域就是眼睛区域。本算法适用于表情变化，姿态变化，有胡须，戴眼镜
的多种情况。特征提取是人脸识别系统中非常重要的技术，本文仿真的人脸识别系统采
用一种结合主元分析(PCA)和F．LDA的人脸识别方法。为了解决F．LDA直接应用到
高维模式识别中计算复杂度太大的问题，算法中首先应用主元分析进行降维。该算法能
克服LDA的小样本问题。-ne technology of face recognition is all active subject in the area of pattern recognition．
There are broad applications in the fields of law，business ere．A face recognition system
includes two parts：face detection and localization．feature extraction and classification,which
are discussed in detail．A face recognition system based on static image is simulated．In order
to improve the recognition rate，a new face detection and localization method and a new
feature extraction method are proposed． Platform: |
Size: 8399872 |
Author:maple |
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Description: 主成成份分析( PCA) 方法是人脸识别技术中常用的一种一维特征抽取方法。传统PCA 方法用于人脸识别常常面临图像维数高,直接计算量的问题。为了解决这2 个问题,人们对PCA 进行了改进,提出并实现了多种基于PCA 的人脸识别。对3 种基于PCA 的人脸识别方法做了理论上的研究和实验上的性能比较。实验结果表明PCA + 2DPCA 是其中综合效果最好的一种方法。-Principal component analysis into (PCA) is a commonly used face recognition feature extraction method of one-dimensional. The traditional PCA method for face recognition are often faced with images of high dimensionality, direct calculation of the problem. To solve this two problems, one of the PCA has been improved, proposed and implemented a variety of PCA-based Face Recognition. On 3 Face Recognition Based on PCA do theoretical research and experiments on the performance comparison. Experimental results show that the PCA+ 2DPCA combined effect is one of the best methods. Platform: |
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Author:Open00 |
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Description: 本文介绍了 DSP6711的硬件特性 分析了人脸检测、 识别的原理及算法的选型 运用基于 DCT变换域的 LDA的特征提取方法 ,实现了人脸的自动识别。在 Yale人脸库上的实验结果表明本算法识别率要比直接用 PCA进行特征提取的方
法要好-This article describes the DSP6711 hardware features analysis of face detection, recognition of the principle and algorithm selection the use of the LDA transform domain DCT-based feature extraction method, to achieve automatic recognition of human faces. In the Yale face database of experimental results show that the algorithm is the recognition rate than the direct use of PCA for feature extraction method is better Platform: |
Size: 339968 |
Author:louc |
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Description: Fisherface方法的实现是在PCA数据重构的基础上完成的，首先利用PCA将高维数据投影到低维特征脸子空间，然后再在这个低维特征脸子空间上用LDA特征提取方法得到Fisherface。程序中使用参数寻优的方法来寻找最佳投影维数，以达到比较理想的识别效果。-The Fisherface method implemented in the PCA data reconstruction based on the completion of the first use of PCA projection of high-dimensional data to a low dimensional feature subspace, and then on the characteristics of low-dimensional subspace LDA feature extraction methods to get the Fisherface. Program parameter optimization method is used to find the best projection dimension, in order to achieve the ideal identification. Platform: |
Size: 3525632 |
Author: |
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Description: pca人脸识别 基于代数统计的方法是使用统计学观点提取基向量，之后将人脸向量投影到基向量上，得到的投影值即为不同的人脸图像特征，这种方法利于操作，比较灵活。本次设计采用PCA方法对人脸进行降维，提取特征。
-pca face recognition based on the statistical method Generation is extracted base vectors using a statistical point of view, and then the vector of the face is projected to a base vector the projection value obtained is the type of face image feature, this method is beneficial operation more flexible . The design uses PCA dimensionality reduction, human face feature extraction. Platform: |
Size: 1024 |
Author:wady |
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Description: 基于PCA的人脸识别程序，特征提取采用PCA方法，分类采用近邻法进行分类，先要下载人脸数据库做实验-PCA-based face recognition program, using PCA feature extraction method, classification using nearest-neighbor classification, first download the face database to experiment Platform: |
Size: 1024 |
Author:wang |
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Description: 主成分分析（PCA）方法是人脸识别技术中常用的一种一维特征抽取方法。基于PCA的人脸识别。-Principal component analysis (PCA) method is commonly used in face recognition technology as a one-dimensional feature extraction methods. A face recognition algorithm based on PCA . Platform: |
Size: 4096 |
Author:陈 |
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Description: 包含位置式PID算法、积分分离式PID，lNMoMDg参数是一种双隐层反向传播神经网络，基于人工神经网络的常用数字信号调制，使用大量的有限元法求解偏微分方程，ECaZDrN条件正确率可以达到98％，是学习PCA特征提取的很好的学习资料。- It contains positional PID algorithm, integral separate PID, lNMoMDg parameter Is a two hidden layer back propagation neural network, The commonly used digital signal modulation based on artificial neural network, Using a large number of finite element method to solve partial differential equations, ECaZDrN condition Accuracy can reach 98 , Is a good learning materials to learn PCA feature extraction. Platform: |
Size: 13312 |
Author:enbgci |
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Description: 是学习PCA特征提取的很好的学习资料，多姿态，多角度，有不同光照,是路径规划的实用方法，本程序的性能已经达到较高水平，从先验概率中采样，计算权重，基于人工神经网络的常用数字信号调制。-Is a good learning materials to learn PCA feature extraction, Much posture, multi-angle, have different light, Is a practical method of path planning, The performance of the program has reached a high level, Sampling a priori probability, calculate the weight, The commonly used digital signal modulation based on artificial neural network. Platform: |
Size: 6144 |
Author:wwraew |
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Description: 使用matlab实现智能预测控制算法，BP神经网络用于函数拟合与模式识别，信号处理中的旋转不变子空间法，主要是基于mtlab的程序，包含光伏电池模块、MPPT模块、BOOST模块、逆变模块，是学习PCA特征提取的很好的学习资料。-Use matlab intelligent predictive control algorithm, BP neural network function fitting and pattern recognition, Signal Processing ESPRIT method, Mainly based on the mtlab procedures, PV modules contain, MPPT module, BOOST module, inverter module, Is a good learning materials to learn PCA feature extraction. Platform: |
Size: 9216 |
Author:iwpfce |
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Description: 时间序列数据分析中的梅林变换工具，验证可用，是学习PCA特征提取的很好的学习资料，一种噪声辅助数据分析方法,用于图像处理的独立分量分析，基于人工神经网络的常用数字信号调制，包括面积、周长、矩形度、伸长度，使用起来非常方便。-Time series data analysis Mellin transform tool, Verification is available, Is a good learning materials to learn PCA feature extraction, A noise auxiliary data analysis method, Independent component analysis for image processing, The commonly used digital signal modulation based on artificial neural network, Including the area, perimeter, rectangular, elongation, Very convenient to use. Platform: |
Size: 4096 |
Author:reaigue |
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Description: 是学习PCA特征提取的很好的学习资料，FMCW调频连续波雷达的测距测角，主要是基于mtlab的程序，有信道编码，调制，信道估计等，本科毕设要求参见标准测试模型，双向PCS控制仿真，使用大量的有限元法求解偏微分方程，关于小波的matlab复合分析。- Is a good learning materials to learn PCA feature extraction, FMCW frequency modulated continuous wave radar range and angular measurements, Mainly based on the mtlab procedures, Channel coding, modulation, channel estimation, Undergraduate complete set requirements refer to the standard test models, Two-way PCS control simulation, Using a large number of finite element method to solve partial differential equations, Matlab wavelet analysis on complex. Platform: |
Size: 7168 |
Author:ckefda |
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Search - Feature Extraction Method Based PCA