搜索资源列表
SIFTSURF
- SIFT算法、SURF算法和PCA—SIFT算法的实例，里面要安装OPENCV才能用的-SIFT algorithm, SURF algorithm and PCA-SIFT algorithm instance, which can be used to install OPENCV
FaceRec
- This example face recognition with opencv using PCA algorithm
FaceReco_PCA3
- 这是一个用VC6编写的PCA人脸识别程序，用到了opencv库，库文件一起上传了，可以直接编译。也有相应的训练与测试图像。感觉效果还可以！-This is a PCA face recognition using VC6 written procedures, use the opencv library, library file with the upload, can be directly compiled. Have corresponding training and test im
PCA
- 人脸识别里面，用c++讲PCA程序系统化，有输出界面-Face Recognition inside, using c++-speaking PCA procedure systematic, there is the output interface
face.tar
- This python scr ipts are for face recognition. I used PCA Algorithm. You need to import few python packages. Forinstance numpy,opencv-python,psyco -This python scr ipts are for face recognition. I used PCA Algorithm. You need to import
pca
- 基于VC的PCA人脸识别，人脸库需要自己加入-The PCA-based Face Recognition VC
PCA
- 在图像处理特别是人脸识别中经常用到PCA算法，这是基于Opencv的PCA算法。-In the image processing in particular are often used in PCA face recognition algorithm, which is based on the Opencv the PCA algorithm.
VCPCA
- c++ pca opencv c++ pca opencv
hythlastlda
- this code use to to make recognition for face using PCA+LDA by opencv
PCA
- face recognition using PCA buy opencv
pca
- this function to load image from any folder using opencv
PCA-and-for-the-face-recognition-
- 主成分分析，及其用于人脸识别的算法实现。-Principal component analysis, and for the face recognition algorithm.
face-opencv-cPP
- 基于opencv 人脸识别 pca 算法 c++的实现 -Pca-based face recognition algorithm opencv c++ implementation
face-rec-demo
- PCA in Opencv using C plus plus. demo
PCA
- This Code is the PCA using the plataform OpenCv
opencv_pca2
- 基于opencv的PCA程序，不是调用opencv的PCA函数，而是利用opencv的读写，根据主成分算法原理而写的！-PCA program based on OpenCV, OpenCV PCA function is not called, but to read and write using OpenCV, based on the algorithm of principal component and write!
PCA
- pca降维代码，主要用来给图片进行降维，程序不长，直接用，很方便(PCA Dimension reduction code)
pca
- 在许多领域的研究与应用中，往往需要对反映事物的多个变量进行大量的观测，收集大量数据以便进行分析寻找规律。多变量大样本无疑会为研究和应用提供了丰富的信息，但也在一定程度上增加了数据采集的工作量，更重要的是在多数情况下，许多变量之间可能存在相关性，从而增加了问题分析的复杂性，同时对分析带来不便。如果分别对每个指标进行分析，分析往往是孤立的，而不是综合的。盲目减少指标会损失很多信息，容易产生错误的结论。 因此需要找到一个合理的方法，在减少需要分析的指标同时，尽量减少原指标包含信息的损失，
PCA人脸识别
- opencv下，用PCA主成分分析的方式做人脸识别(Using PCA principal component analysis to do face recognition)