WPSNR

snake例子，可以帮助初学者深入具体的对SNAKE 的架构和算法思想进行理解(snake example, can help beginners depth of the Snake concrete structure and algorithm for understanding)

该程序功能是实现对道路流动车辆统计。小模块功能有以下几个：1，对视频解帧并显示。2，对该视频训练背景图片。 3，利用背景图片用减背景法对测试视频进行前景提取，并进行阈值处理，图像分割，找出流动车的连通域。 4，对车辆进行统计。程序包内含有界面说明，以及程序使用的步骤和相关注意事项(The program function is to achieve movement of vehicles on the road statistics. A small module function has the following: 1 solution for video frames and displayed. 2, the video training background image. 3, using the background image used by the background method of testing the prospects for video extraction, and conduct threshold processing, image segmentation, to identify mobile car-connected domain. 4, vehicle statistics. Interface package contains instructions and procedures related to the use of the steps and considerations)

说明：  Delphi利用OpenCV实现的视频录像单元(Implementation of capture camera to avi by OpenCV in Delphi.)

SAR图像和光学图像的配准算法！！！matlab源码~~~~绝对好！(SAR image and optical image registration algorithm! ! ! matlab source code ~ ~ ~ ~ definitely good!)

利用模糊C均值图像分割,此为matlab环境下的仿真代码.(Using fuzzy C-means image segmentation, this matlab code simulation environment.)

基于rtklib计算加入北斗的单点定位及差分定位(single PP and DGNSS using BEIDOU based on RTKLIB)

说明：  学习鬼成像原始程序。经验证可以跑通，欢迎交流。(Learn the original program of ghost imaging. After verification, it can run through. Welcome to communicate.)

说明：  实现图像精确分割，基于ALpha Matting 的基础上的Sharedmatting图像分割算法(Accurate image segmentation,Sharedmatting image segmentation algorithm based on ALpha Matting)

一种快速的图像拐点的matlab程序，在一张图像上用9点快速检测，要求被处理图像为2值图像。(a fast image inflection point of Matlab procedures, in an image with 9:00 rapid detection, Image processing was required for two binary images.)

主成分分析 （ Principal Component Analysis ， PCA ）或者主元分析。是一种掌握事物主要矛盾的统计分析方法，它可以从多元事物中解析出主要影响因素，揭示事物的本质，简化复杂的问题。计算主成分的目的是将高维数据投影到较低维空间。给定 n 个变量的 m 个观察值，形成一个 n ′ m 的数据矩阵， n 通常比较大。对于一个由多个变量描述的复杂事物，人们难以认识，那么是否可以抓住事物主要方面进行重点分析呢？如果事物的主要方面刚好体现在几个主要变量上，我们只需要将这几个变量分离出来，进行详细分析。但是，在一般情况下，并不能直接找出这样的关键变量。这时我们可以用原有变量的线性组合来表示事物的主要方面， PCA 就是这样一种分析方法。(Principal component analysis (Principal Component Analysis, PCA) or PCA. Is a statistical method to grasp the principal contradiction of things, it can be resolved diverse things out the main factors, revealing the essence of things, simplifying complex problems. The purpose of calculating the main component of high-dimensional data is projected to a lower dimensional space. Given n variables of m observations, forming an n ' m of the data matrix, n is usually large. For a complex matters described by several variables, it is difficult to know, so if you can grab something to focus on key aspects of analysis? If the main aspects of things just reflected on several key variables, we only need to separate out these few variables, for detailed analysis. However, in general, does not directly identify this critical variables. Then we can represent the major aspects of things with a linear combination of the original variables, PCA is one such analysis.)