R Graph Essentials

利用svm算法分类图像。示例中：将篮球场和网球场利用算法自动分类，包含了基于颜色矩的彩色图像特征提取算法(image classification using SVM algorithm)

改程序就代码可以找出从不同角度或区间的两幅遥感影像的同名点(Change the program code can find it from different angles of the same name point or interval of two remote sensing images)

本程序为基于快速双边滤波器的边缘保持算法，算法具有平移不变性，具有线性复杂度(this is a edge-preserving denoising method using fast bilateral filter)

说明：  在VC中实现了使用Moravec算子对遥感影像进行特征点的提取，缺点是速度较慢。不过提取效果不错！(Implemented in VC using Moravec operator of remote sensing images feature point extraction, the disadvantage is slow. However, extracting good results!)

基于matlab的RANSAC算法，主要用来拟合二维直线，同时选出非直线的点用于后续曲线拟合。(The RANSAC algorithm based on MATLAB is used to fit two dimensional lines, and the point of non straight line is selected for the subsequent curve fitting.)

fluent软件UDF，焊接高斯移动热源。(weld heat source)

说明：  在MFC中显示各种格式的图片，jpg.png(Display pictures in various formats in MFC, jpg.png)

粒子滤波的五个经典例子，需要的赶紧下，对与学习部分内容很有帮助(Particle filter five classic example of the need to hurry, the right part of the contents and learning very helpful)

本压缩文件利用Matlab制作了一个能识别居民身份证号码的系统，使用图像处理技术，实现了计算机识别数字的功能。(The compressed file using the Matlab made a system can identify the identity card number, using image processing technology, realized the function of the digital computer identification. )

转：https://blog.csdn.net/jinshengtao/article/details/26278725 混合高斯背景建模是基于像素样本统计信息的背景表示方法，利用像素在较长时间内大量样本值的概率密度等统计信息(如模式数量、每个模式的均值和标准差)表示背景，然后使用统计差分(如3σ原则)进行目标像素判断，可以对复杂动态背景进行建模，计算量较大。 在混合高斯背景模型中，认为像素之间的颜色信息互不相关，对各像素点的处理都是相互独立的。对于视频图像中的每一个像素点，其值在序列图像中的变化可看作是不断产生像素值的随机过程，即用高斯分布来描述每个像素点的颜色呈现规律单模态(单峰)，多模态(多峰)(Gaussian mixture background modeling is a background representation method based on the statistical information of pixel samples. Statistical information such as the number of patterns, the mean and standard deviation of each pattern are used to represent the background. Statistical difference (such as 3_principle) is used to judge the target pixel. Complex dynamic background modeling has a large amount of computation. In the Gaussian mixture background model, it is considered that the color information between pixels is uncorrelated and the processing of each pixel is independent of each other. For each pixel in a video image, the change of its value in a sequential image can be seen as a random process that produces pixel values continuously, i.e. Gaussian distribution is used to describe the regularity of color rendering of each pixel in single mode (single peak) and multi-mode (multi-peak).)