图像得简单编辑，有兴趣得可以

辐射度模型，3D图形学当中有关辐射度模型的具体描述-Radiosity+-+A+Programmer_ s+Perspective

基于Kalman滤波目标跟踪算法，用matlab实现，已验证，效果良好，带素材。

The drawing tool will allow you to draw lines, rectangle, ellipse, and has pen and eraser as tools. It also allow region filling with color

//Name:geotrans.c//Purpose: To perform geometric transform//Author: phoenix, CS, TshingHua, Beijing, P.R.C.//Email: bjlufengjun@www.163.net or lufengjun@hotmail.com//Date:April 3, 1998

本文章主要介绍了相机标定的过程，并给出相应的标定内容。-This article mainly introduces the process of camera calibration, and gives the contents of the corresponding calibration.

一个半透明的图像叠加的演示，利用Windows API将一个较小的图像叠加到一个大图像上并且小图像可以移动-a translucent image superimposed demonstration using Windows API to a smaller image of superimposed images on a large and small images can be mobile

数字识别代码 第一步：训练网络。使用训练样本进行训练。（此程序中也可以不训练，因为笔者已经将训练好的网络参数保存起来了，读者使用时可以直接识别）第二步：识别。 首先，打开图像（256色） 再次，进行归一化处理。点击“一次性处理” 最后，点击“R”或者使用菜单找到相应项来进行识别识别的结果显示在屏幕上，同时也输出到文件result.txt中 该系统的识别率一般为90％ 另外，也可以单独对打开的图片一步一步进行图像预处理工作。但要注意，每一步工作只能执行一遍，而且要按顺序执行。步骤为：“256色位图转为灰度图”“灰度图二值化”“去噪”“倾斜校正”“分割”“标准化尺寸”“紧缩重排” 另外注意，要识别的图片，要与win.dat、whi.dat位于同一目录。此两文件保存训练后网络的权值参数 -digital identification code first step : training network. Use of training samples for training. (These procedures also can not training because I have trained network parameters saved, Readers can direct identification) second step : identification. First, open the Image (256 colors) again, to a treatment. Click on the "one-stop" Finally, Click on the "R" or use the menu item corresponding to find the identification of the identification results on the screen, also result.txt output to a file which the system"s recognition rate of 90% for the other, can be individually opened to the step-by-step pictures for image prep

在matlab下实现分水岭算法，经测试成功运行，供学习数字图像处理的朋友参考-Matlab achieved in the watershed algorithm, have been tested to run successfully for the study of digital image processing friend reference

这份代码经源码格式软件格式化过-this format source code by formatting software too

这是一个比较好的基于主元分析PCA的代码，个人觉得很有价值。-This is a good principal component analysis PCA-based code, personally feel very valuable.