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
PCA--learning
- PCA数学基础介绍，以及附带用opencv写的测试程序-PCA mathematics foundation introduction, and additional opencv write with the test procedures
PCA
- face recognition using PCA buy opencv
pca
- this function to load image from any folder using opencv
PCARecognition
- A novel algorithm for Face Detection using PCA algorithm. Library used: OpenCV2.0
face-rec-demo
- PCA in Opencv using C plus plus. demo
part1-src
- PCA in Opencv. how to use egeinface
PCA
- This Code is the PCA using the plataform OpenCv
pca
- 实现pca功能，进行数据降维，使算法简单化-Realize pca functions, to data dimension reduction, the method is simple
pca
- pca算法的matlab实现，进行数据的降维处理-Pca algorithm matlab, the data dimension reduction processing
pca
- 模式识别里面的经典PCA算法，用OpenCV视觉开发库实现的。该算法主要用于训练分类器，然后来对人脸来进行识别。-PCA algorithm developed with 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-based-on-OpenCV-and-cPP-
- 一篇用于理解PCA主成分分析的文章，该文章用基于Opencv和C++的源码对PCA进行说明。-One used to understand the PCA principal component analysis of the article, the article described the PCA-based the Opencv and C++ source.
pca
- 基于Opencv和Visual C++ 6.0的PCA实现-Based on Opencv and Visual C++ 6.0 PCA realized
Face-recognition-based-on-PCA
- 基于vc++6.0和opencv1.0的单样本人脸识别。用了PCA和2DPCA降维，效果不错。附带样本-Face recognition based on the vc++6.0, and opencv1.0 single sample. With the PCA and 2DPCA dimensionality reduction, good results. Comes with sample
PCA
- 自己写的PCA降维程序，基于opencv，项目里使用过，现在奉献出来。-PCA dimensionality reduction program based on opencv
OpenCV-PCA-face-dimension-reduction
- OpenCV中PCA实现人脸降维，基于QT实现-OpenCV PCA face dimension reduction
pca
- 这是一段pca代码，对于进行人脸识别是一个很好的方法。-This is a pca code, for face recognition is a very good method.
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，