goldstein_filt_revised

说明：  face recognition example for learning

可以用于图像分割的KNN分类器，简单好用！(knn classification)

摄影测量中影像匹配小程序，附带两幅图片,比较实用(Photogrammetry image matching small program, with two pictures, more practical)

本程序是用似然比检验的方法进行profile中的变点检测(This procedure is used likelihood ratio test method of change point detection profile)

程序设计步骤： 1、去均值 2、计算协方差矩阵及其特征值和特征向量 3、计算协方差矩阵的特征值大于阈值的个数 4、降序排列特征值 5、去掉较小的特征值 6、去掉较大的特征值(一般没有这一步） 7、合并选择的特征值 8、选择相应的特征值和特征向量 9、计算白化矩阵 10、提取主分量(Programming steps: 1 to mean 2, calculation of covariance matrix and its eigenvalues and eigenvectors 3, the calculation of covariance matrix eigenvalues greater than the threshold number of 4, in descending order of eigenvalue 5, remove the smaller eigenvalue 6, remove the larger eigenvalue (usually not this step) 7, the combined choice of eigenvalues 8, select the appropriate eigenvalues and eigenvectors 9, computing whitening matrix 10, principal component extraction)

demons三维非刚性图像配准，速度很快，基于ITK(demons of 3D non-rigid image registration, very fast, based on ITK)

说明：  用jpeg对图像进行编码解码，里面包含界面，以及图片(Using JPEG to encode and decode the image)

一个简单的基于HOG的行人检测程序，MATLAB编写，其中read me文档解释的较为详细。(A simple HOG based pedestrian detection program, MATLAB write, read me the document which explains in some detail.)

运用harr特征+SRC（稀疏表示）分类实现的一种车辆检测方法，文件中提供了训练和测试车辆图片。由于时间原因，所用haar特征没有优化，维度过高，导致滑窗框图过慢，本代码只输出效果统计数据，以供大家参考学习稀疏表示在车辆检测中的应用。(Using harr feature+SRC (sparse representation) classification to achieve a vehicle detection method, the paper provides a training and test vehicle picture. Due to time reasons, the use of haar feature is not optimized, high dimension, resulting in sliding sash figure is too slow, the effect of the code only output statistics for your reference learning sparse representation in the vehicle detection.)

利用正交匹配跟踪原子库对信号进行稀疏分解程序(Using orthogonal matching pursuit atom libraries for signal sparse decomposition process)