搜索资源列表
numberplate_C++Builder
- 车牌识别C++Builder代码，首先要把车牌图像灰度化，接着对图像进行中值滤波进行初步降噪，下一步进行Sobel纵向边缘检测，即增强车牌纵向边缘，边缘检测后进行二值化处理，此时车牌区域特征得到进一步加强，但同时又加强了背景中的部分噪声，所以再对其腐蚀，然后定位及截取车牌，最后对车牌进行二值化。 -LPR C Builder code, first and foremost, we should plates gray, Then the image median filtering for
NoiseReduction_v1
- 本程式实做了Noise Reductin Algorithm 2005年一个韩国人在ieee发表的Paper Block-based noise estimation using adaptive Gaussian filtering 此论文也收录在资料夹中，供使用者参考。 首先，click open file， 选择第一张原始图(未被杂讯污染) 接下来选择第二张图(有杂讯) 你可以直接点选Noise Reduction观看结果 也可以先调整旁边的th
attfaces.zip
- 机遇PCA的人脸识别，包括图像读取，PCA降维以及机遇简单贝叶斯分类,PCA Face Recognition opportunities, including the image read, PCA dimensionality reduction as well as the opportunity for easy Bayesian Classifier
DCC
- 基于典型相关性的线性鉴别分析，先用PCA对数据降维之后，再结合典型相性鉴别分析来得到转换矩阵-Based on a typical linear correlation analysis to identify, first PCA for data reduction, the combined analysis to identify the typical phase of the transformation matrix obtained
PCAPSVM
- PCA+SVM，对图像进行降维分类，并在yale库上测试取得比较好的效果-PCA+ SVM, dimensionality reduction of image classification, and yale library to achieve better test results
PCAKPCA
- PCA和KPCA程序，matlab实现，可用于模式识别时做降维或特征提取处理-PCA and KPCA program, matlab implementation, pattern recognition can be used to do when dealing with dimensionality reduction or feature extraction
lpp
- Laurens van der Maaten 编写的LPP算法，与 Deng Cai版本不同，大家比较下-This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7b. (C) Laurens van der Maaten Tilburg University, 2008
pca
- pca算法的matlab实现 主成分分量分析可用于数据的降维和模式识别问题 -pca algorithm matlab component analysis to achieve the principal component can be used for data dimensionality reduction and pattern recognition problem
lda
- lda 源码。用来为数据降维，使得识别率有所提高。-lda source code. Is used for data dimensionality reduction, making the recognition rate increased.
CCA_zq
- 用于特征降维，特征融合，相关分析等多元数据分析的典型相关分析Matlab代码实现。-For feature reduction, feature fusion, correlation analysis, multivariate data analysis, canonical correlation analysis of Matlab code implementation.
DCCA_zq
- 用于特征降维，特征融合，相关分析等多元数据分析的鉴别型典型相关分析（DCCA）Matlab代码实现。-For feature reduction, feature fusion, multivariate data analysis and correlation analysis based identification of canonical correlation analysis (DCCA) Matlab code implementation.
GCCA_zq
- 用于特征降维，特征融合，相关分析等多元数据分析的广义典型相关分析（GCCA）Matlab代码实现。-For feature reduction, feature fusion, correlation analysis, multivariate data analysis using generalized canonical correlation analysis (GCCA) Matlab code implementation.
LDA_zq
- 用于特征降维，特征融合，相关分析等多元数据分析的fisher鉴别分析（FLDA）Matlab代码实现。-For feature reduction, feature fusion, correlation analysis, multivariate data analysis of the fisher discriminant analysis (FLDA) Matlab code implementation.
PCADR
- 用于特征降维人脸识别等多元数据分析的主分量分析投影的Matlab代码实现。-For feature reduction and other multivariate data analysis, face recognition principal component analysis projection of the Matlab code implementation.
regularized-lda
- 正则化LDA，最新！对于图像降维与图像识别的研究有很重要的研究意义-Regularized LDA, the latest! Dimension reduction for images and image recognition of a very important research significance
SubspaceLearningCodes(Matlab)forFaceRecognition.ra
- 人脸识别的子空间方法，包括LLE，ISOMAP和NPE等维数约减算法-Subspace method for face recognition, including LLE, ISOMAP and NPE dimensionality reduction algorithm such as
GaborLLE
- 实现了基于Gabor和LLE的人脸识别，在ORL数据库上有较好的效果-The code implement the facerecognition based on Gabor feature and LLE dimension reduction.
PR_paper
- 基于独立成分分析的人脸识别方法研究 基于流形学习的数据降维方法研究 人脸识别系统的设计与开发 基于主成分分析的人脸识别 特征提取算法的研究与改进。。。。。。 做人脸识别式一系列的参考资料 -Face recognition based on independent component analysis method based on manifold learning method of data reduction system design and developme
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
fsvmPpca-face-Recognition
- 首先用PCA对ORA人脸图像降维，然后用模糊支持向量机对提取的特征向量进行分类，识别率较高。-First using PCA for dimensionality reduction ORA face image, and then use fuzzy support vector machine to classify the extracted feature vectors, the recognition rate is higher.