vibemotiondetection

基于opencv实时图像的2值化处理，一定要有摄像头(Opencv-based real-time image processing of two values, there must be a camera)

实现一些图像的编辑功能，比如图像的打开，关闭，新建，删除，反转，等一些功能(Implement some image editing features, such as the image open, close, create, delete, reverse, and some other functions)

实现了灰度共生矩阵，并应用到了几张图片上。 C++ 和OPENCV 代码。 图像处理(GLCM achieved and applied to a few pictures. C++ and OPENCV code. Image Processing)

这个实例就像一个魔镜一样，能把原始图像照出各种变换效果,是基于MFC的多文档应用程序(This example is like a mirror,which can convert original image according to a variety of effects)

重要的纹理分割分类基础程序，可以进行更深入的探讨和优化，应用于遥感影像中。(Important basis for classification of texture segmentation procedure can be carried out more in-depth exploration and optimization, application of remote sensing images.)

全屏显示窗体。通过快捷键F11实现程序全屏显示的效果。(Full-screen display of the form. Through the shortcut key F11 to achieve the effect of full screen display.)

说明：  图形图像出来库文件，挺好用的，直接包含进工程就可以用了 (Graphic images from the library files, good used directly included into the project can be used)

实现凸多边形排样，使用临界多边形NFP判断两个凸多边形是否相交,使用启发式算法提高效率(It s a good implement of convex polygon stock.)

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)

一幅大小为256×256的256级灰度图像（每像素8bit） 如果图像的各个像素之间是统计独立的，求该图像的信息熵 -离散无记忆信源 如果图像的各个像素之间不是统计独立的，求该图像的信息熵 -图像像素的概率分布模型(A 256 gray scale image (8bit per pixel) with a size of 256 x 256 If each pixel of the image is statistically independent, the information entropy of the image is calculated Discrete memoryless source If each pixel of the image is not statistically independent, the information entropy of the image is calculated Probability distribution model of image pixels)