人脸识别源代码，经过修改，希望对大家有一些参考价值。

用matlab编写的图像复原源代码，采用维纳滤波复原技术，可对模糊和加噪的图像实现精确复原。-Prepared using matlab source code image restoration using Wiener filter restoration technology, can be vague and processors realize accurate image noise resilience.

数字图象处理的应用--人脸检测,通过VC提取特征并完成检测-Application of Digital Image Processing- Face detection, through the extraction of the characteristics of VC and complete testing

完整的程序，提供测试视频帧。根据视频帧中图像的形状进行运动估计-integrity of the procedures for testing video frames. According to Video Frame Image Motion Estimation shape

3x3中值滤波器的FPGA实现(VERILOG)-3x3 median filter FPGA implementation (VERILOG)

基于visual c++ 2005实现 ColorPicker是一个基于对话框的应用程序，它具有以下功能： 1. 色彩编辑功能 用户可以通过可以调整R、G、B的值来编辑颜色，亦可通过调整H、S、V的值来选取颜色。颜色编辑的结果会马上反馈到颜色面板和颜色预览框中。 2.RGB颜色空间和HSV颜色空间的转换 当改变RGB值，会得到相应的HSV值，并进行显示，反之亦然。 3.取色功能 取色功能包含“面板取色”和“屏幕取色”。面板取色就是用户可以在颜色面板中单击鼠标左键，选取目标点所表示的某种颜色。屏幕取色则是允许用户获取整个屏幕上的任意一点的颜色值。用户可以将鼠标移动到需要获取颜色的地方，然后按a键或A键即可以获取该点颜色值。-err

基于Graph Cuts计算机视觉立体视图匹配算法-Computer Vision Graph Cuts Based on three-dimensional view matching algorithm

实现各种基础的图象处理功能，例如傅里叶变换，图像滤波，平滑锐化，直方图-based on the realization of various image processing functions, such as Fourier transform, image filtering, smoothing sharpening, histogram

photoshop水晶苹果的制作 很好看的-Crystal Apple produced very interesting

这是一个凸多边形中轴算法：解压后在vc++6.0中运行即可，鼠标逆时针在客户区取点，如顺时针取点或形成的多边形为凹多边行，程序会自动退出（详情可参见代码）-This is a convex polygon axial algorithm : unpacked in vc 6.0 can be run, mouse anti-clockwise from the customer area, as a clockwise or take the form of concave polygon multilateral OK, the program will automatically withdraw from the (details can be found in code)

（1）应用9×9的窗口对上述图象进行随机抽样，共抽样200块子图象； （2）将所有子图象按列相接变成一个81维的行向量； （3）对所有200个行向量进行KL变换，求出其对应的协方差矩阵的特征向量和特征值，按降序排列特征值以及所对应的特征向量； （4）选择前40个最大特征值所对应的特征向量作为主元，将原图象块向这40个特征向量上投影，所获得的投影系数就是这个子块的特征向量。 （5）求出所有子块的特征向量。 -(1) the application of 9 × 9 window of these images at random, a total sample of 200 sub-image (2) all sub-images according to out-phase into a 81-dimensional row vector (3) all 200 lines for KL transform vector, derived its corresponding covariance matrix of eigenvectors and eigenvalues, in descending order by eigenvalue and the corresponding eigenvector (4) a choice to 40 corresponding to the largest eigenvalue eigenvector as the PCA, the original image block to the 40 feature vectors on the projection, the projection coefficients obtained by this sub-block eigenvector. (5) calculated for all sub-block eigenvector.