MRF_SEGMENT

多目标检测与跟踪算法的实现，matlab程序，各个函数分开写的，便于调用(Many target detection and tracking algorithm, the realization of the matlab, each function, easy to call the write it separately )

RANSAC算法实现数据拟合，用此算法可以实现多视角图像匹配等(RANSAC algorithm for data fitting, using this algorithm can realize multi-angle image matching, etc.)

图像基础处理——透视矫正的程序代码，把图像还原(Image basic processing- perspective correction code to restore image)

说明：  经典的Tarel去雾算法，可用于图像去雾的指标性能对比。(It is the classical dehazing algorithm, and it is can be used comparison with other algorithms.)

计算机视觉经典著作《计算机视觉中的多视图几何》配套代码(MATLAB and Octave Functions for Computer Vision and Image Processing)

在做图像处理时用遇到图像的阈值分割，这是几种图像的阈值分割方法(In doing image processing with experience image threshold segmentation, which is one of several image thresholding segmentation method)

初学者的游程编码，可以进行小图像的压缩，可以以此为基础进行编码和参考(RLE beginner, you can make small image compression, can be encoded as a basis and reference)

YUV视频文件二维运动估计中的三步法源程序，可以直接运行！(Three Step Search)

总变分法TV模型代码domo，部分代码，对图像恢复，去噪有一定作用(Variational method of total TV model code domo, some code, for image restoration, denoising have a certain role)

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).)