本代码为王晓东算法书上的多边形代码贪心算法实现

关于meanshift算法的详细介绍，包括中英文，对初学者有一定启发-Meanshift algorithm on the details, including English, have some inspiration for beginners

snake模型的C源码，采用欧拉方程求解模型的能量最小值，实现了经典的活动轮廓模型-snake 模 would like to have 的 C 码, 采 sputum 欧拉方程求解模 would like ghosts from the 的能量 Chiating Jun 了经典的活动轮廓模 would like to

模式识别的特征识别可有多种方法，同时每种方法都有着一定的局限性，这里给出一种比较通用的方法来进行识别-Pattern Recognition feature recognition can have a number of ways, while each method has certain limitations, here given a more general approach to identify the

游戏中的下雨效果,模拟得十分像,强烈推荐.-games of the rain effects, as simulation is very strongly recommended.

image render colour processing-render color image processing

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

该程序是用于图像处理的，包括对位图进行滤波，阈值分析等功能-that the procedure was used for image processing, including bitmap filtering, threshold analysis, and other functions

（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.

跳白块法内核 job4为主函数，这个我找了好久 那里都没有的-jump white block job4 mainly France kernel function, I find this for a long time there were no

本程序在对图像进行纹理分析（由于共发矩阵的方法效果很不好，本程序采用基于频率域的纹理分析算法）的基础上，获取图像不同区域的纹理特征，针对这些纹理特征，采用聚类（K-mean)的分类算法对图像进行区域划分！-procedures in the right image texture analysis (due to a total of hair matrix, the effect is very bad, the program uses a frequency domain based on the texture analysis algorithm), on the basis of access to different regions of the image texture features, these texture characteristics, using clustering (K-mean) the right image classification algorithm for a regional breakdown!