PCA 主成份分析法 用于人脸识别中的图像处理方法

这是一个基于opencv的sift算法的实现-This is a sift of opencv algorithm based on the realization of

采用Visual c++ 进行图像处理，已经经过检测，非常实用，本源码主要是高斯平滑-for image processing, have been tested, the very practical, The main source of Gaussian smoothing

基础线性分类器的检测用于分离超平面是单向垂直的。- TESTING THRESHOLD CLASSIFIER

bivalueis about image

霍夫曼编码的具体方法：先按出现的概率大小排队，把两个最小的概率相加，作为新的概率 和剩余的概率重新排队，再把最小的两个概率相加，再重新排队，直到最后变成1。每次相 加时都将“0”和“1”赋与相加的两个概率，读出时由该符号开始一直走到最后的“1”， 将路线上所遇到的“0”和“1”按最低位到最高位的顺序排好，就是该符号的霍夫曼编码。-Huffman code specific Methods: according to their probability of emergence of the size of the queues, the two smallest sum probability, as a new probability and the probability of the remaining re-queue, and then the smallest sum of two probabilities, and then re-queue until the last turn into one. Every time when the sum will be

来源于网上的C++技术开发的图形处理代码, 为国外开源代码, 可用来学习掌握C++的图形处理算法。来源于网上公开资料，供学习使用。-From online C++ Graphics technology development deal with code, for foreign open-source code, can be used to study and master the C++ Graphics processing algorithms. Disclosure of information from the Internet for learning to use.

opencv 读图像,feichangbucuo-opencv

绘制一个桌子并对其进行填充 实现消隐效果 可以旋转-drawing a table and filled its shadow effect can be achieved rotation

基于IBR的图像拼接算法,利用傅里叶变换求相关，可利用多幅图像拼接得到的全景图。-image mosaic based on image based rendering

（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.