搜索资源列表
KL-PCA
- The person face distinguishes the PCA code, MATLAB writes. ,matlab-The person face distinguishes the PCA code , MATLAB writes. , Matlab
PCA-code
- PCA原代码，可以移植到别的pca算法中使用，具有较强的可移植性。-PCA original code can be transferred to other pca algorithm used, with a strong portability.
包括常用的IHS,PCA 加权图像融合三种算法的Matlab源代码
- 包括常用的IHS,PCA 加权图像融合三种算法的Matlab源代码,Which are used in the IHS, PCA-weighted Image Fusion Algorithm three Matlab source code
pca.rar
- 自己写得特征PCA降维的代码，性能非常不错,PCA code for dimension reduction
PCA-code
- 基于主成分分析方法的人脸重构，使用ORL人脸数据库-the face reconstruction based on PCA method
pca-sift
- sift code,希望对大家有用-sift code............................
pca
- 主成分分析程序,应用于图像特征提取，数据降维等方面 -the code of PCA
pca
- PCA代码 主成分分析代码 适合初学人脸识别的朋友学习使用-PCA principal component analysis source code suitable for beginner learning to use face recognition friend
pca
- PCA主分量分析法的MATLAB源代码,用于图像融合中-PCA code
PCA
- 主分量分析方法的应用越来越多，它是怎样实现的呢？本代码就是其实现过程。-Principal component analysis method, more and more how it is achieved it? This code is the implementation process.
PCA-(ICA)
- 主成分分析程序包，包括主成分分析和独立主成分分析两个程序源代码。-Principal component analysis package, including principal component analysis principal component analysis and independent source code for both procedures.
pca
- 这段代码是基于pca方法的数字图像处理技术，具有实际用应价值-This code is based on the method pca digital image processing technology, with the actual values used should be
pca
- Principal Component Analysis源码，程序附带selfdemo演示-pca code
PCA
- 基于PCA改进实现人脸识别的完整代码,采用ORL人脸库进行训练和测试.-Face Recognition Based on the PCA to improve the achievement of a complete code, ORL face database used for training and testing.
pca
- pca 特征提取的源代码，对人脸识别很有帮助，-pca feature extraction of the source code, useful for face recognition,
pca
- pca lda knn 进行分类的pca部分的代码-pca lda knn classification of the pca part of the code
IPCA-VC
- 这是一个增量主成分分析的VC环境下的C代码程序，希望对大家有帮助！-This is an incremental principal component analysis of C code under VC program, we want to help!
pca
- PCA sourece / PCA code file
pca-code
- 图像处理应用PCA可以进行降维和特征能够抽取。-PCA for image processing applications to reduce the dimensions and characteristics can be extracted.