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rtejfgds
- 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 th
DIPDemo
- 《数字图像处理与机器视觉:Visual C++与Matlab实现》7 V图像的点运算,几何变换, 图像增强,彩色图像处理,实用案例——汽车牌照的投影失真校正-" Digital image processing and machine vision: Visual C++ and Matlab to achieve" 6 support vector machines, comprehensive case- based on PCA and SVM for Face Re
eyePicturePCA(Matlab)
- 线性判别方法，眼睛图像数据库上的PCA程序，基于Matlab，供学习使用-Linear Discriminant method, eye image database on the PCA procedure, based on Matlab, for learning to use
detection1
- ,从所得二值图像中获得眼睛的候选 位置 用PCA 方法进行眼睛位置的最佳选择1 该方法解决了二值化阈值步长的自适应选取问题-From the obtained binary image in the eyes of the candidate to obtain Position using PCA method the best choice for an eye position of the solution to the two threshold values of the
principal_components
- This tutorial is designed to give the reader an understanding of Principal Components Analysis (PCA). PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common techn
fisherface
- Eigenfaces: PCA tends to find a p-dimensional subspace whose basis vectors correspond to the maximum variance direction in the original image space (p N). We called the new subspace defined by basis vectors “face space”. First, all training
Infrared-Face-Recognition
- 这篇文章是我实验室写的基于2DPCA的红外人脸识别方法，这里面系统的阐述了使用2DPCA算法比传统PCA算法的好处，本文还阐述了红外图像在人类识别方面独特的优点及存在的不足。-This article is written in my laboratory face recognition method based on infrared 2DPCA, there is a systematic exposition of the use of algorithms than the tradi
palmprint-recognition-
- 首先计算掌纹图像二维Gabo r小波变 换系数的幅值, 将其作为掌纹图像的原始特征 其次, 利用 2DPCA 实现原始特征的降维 然后, 利用PCA 与FLD 的融合算 法进行二次降维的同时提取出最有利于分类的鉴别特征 最后, 利用最近邻算法进行掌纹的分类识别。 -First calculate the two-dimensional Gabo r palmprint image wavelet coefficients of magnitude, its character
fwdcode
- here i have attatched codes for pca image processing
sift
- 1 SIFT 发展历程 SIFT算法由D.G.Lowe 1999年提出，2004年完善总结。后来Y.Ke将其描述子部分用PCA代替直方图的方式，对其进行改进。 2 SIFT 主要思想 SIFT算法是一种提取局部特征的算法，在尺度空间寻找极值点，提取位置，尺度，旋转不变量。 3 SIFT算法的主要特点： a) SIFT特征是图像的局部特征，其对旋转、尺度缩放、亮度变化保持不变性，对视角变化、仿射变换、噪声也保持一定程度的稳定性。 b) 独特性(Distinctive
PCA_Thesis_USM
- In order to fulfill the implementation of this project there are three main objectives that need to be achieved: 1. To learn and apply a technique for object recognition for single and multiface detection. 2. To examine the principal compon
CodesaImages
- 用于指纹检测等，利用图像的梯度方向，获得局部主导方向。Principal Component Analysis （PCA），包含有高斯金字塔分层，SVD奇异值分解，内含测试图像-Used for fingerprint detection, etc. Using the gradient direction of image to get local leading direction. Principal Component Analysis (PCA), contains a gaussi
principal_components
- This tutorial is designed to give the reader an understanding of Principal Components Analysis (PCA). PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common techn
MDS
- pca principal component analysis with medical image-principal component analysis with medical image can make the dimension of the image as short as possible which is easy for us to compare their similiarities
2011_7_1_49_56
- Support vector regression has been proposed in a number of image processing tasks including blind image deconvolution, image denoising and single frame super-resolution. As for other machine learning methods, the training is slow. In this paper,
859398
- Support vector regression has been proposed in a number of image processing tasks including blind image deconvolution, image denoising and single frame super-resolution. As for other machine learning methods, the training is slow. In this paper,
00913592
- Support vector regression has been proposed in a number of image processing tasks including blind image deconvolution, image denoising and single frame super-resolution. As for other machine learning methods, the training is slow. In this paper,
00941854
- Support vector regression has been proposed in a number of image processing tasks including blind image deconvolution, image denoising and single frame super-resolution. As for other machine learning methods, the training is slow. In this paper,
01262011
- Support vector regression has been proposed in a number of image processing tasks including blind image deconvolution, image denoising and single frame super-resolution. As for other machine learning methods, the training is slow. In this paper,
baker_simon_1999_3
- Support vector regression has been proposed in a number of image processing tasks including blind image deconvolution, image denoising and single frame super-resolution. As for other machine learning methods, the training is slow. In this paper,