TextAnalysis

实现凸多边形排样，使用临界多边形NFP判断两个凸多边形是否相交,使用启发式算法提高效率(It s a good implement of convex polygon stock.)

visualc++6.0 实现图像中圆的检测(circle detection)

静态画图，可以改变图形边框颜色与矩形或椭圆的填充颜色(Static drawing, you can change the fill color graphics border color and rectangular or oval)

RAw格式图片的显示，边缘检测，包括SOBEL算子，具有不同模板的拉普拉斯算子边缘检测，以及一些简单的滤波方式(sadfsadf asdfasdf asdf)

直方图均衡化处理编程指南及实验报告，数字图像处理及应用，阮秋琦教授，北京交通大学（资源分享）。(Histogram equalization processing programming guide and laboratory reports，Application of digital image processing and Beijing Jiaotong University, Professor Ruan Qiuqi)

基于OPENCV的SIFT特征提取与匹配算法。包含完整的从图像高斯金字塔、DOG、空间极值点提取、关键点描述、KDtree匹配等关键步骤的全部函数实现，对全面深入理解Lowe的SIFT算法有莫大帮助。程序运行前须安装（1）OpenCV: http://opencvlibrary.sourceforge.net （2）SIFT: http://web.engr.oregonstate.edu/~hess/index.html，并配置其环境参数。(OPENCV the SIFT-based feature extraction and matching algorithm. Contains a complete Gaussian pyramid from the image, DOG, space extremum point extraction, description of key points, KDtree matching key step in the realization of the full function of the comprehensive and in-depth understanding of Lowe s SIFT algorithm of tremendous help. Program to run before the installation of (1) OpenCV: http://opencvlibrary.sourceforge.net (2) SIFT: http://web.engr.oregonstate.edu/ ~ hess/index.html, and configure the parameters of their environment.)

利用数字图像处理软件halocn来进行缺陷检测，附带有之前做工程项目的原图源代码，还有ppt和实验指导书(Halocn using digital image processing software for defect detection, comes with artwork done before the project source code, as well as ppt and experimental instructions)

本文介绍了一种测量大气能见度的方法，双亮度差法，代码中给出了详细的注释，并且包括OSTU方法等等(In this paper, a measuring method of atmospheric visibility double brightness difference method, the code is given detailed annotations, and including the OSTU method and so on)

激光三维点云的处理源码，可以压缩点云，配准点云(Three-dimensional laser point cloud processing source code, can be compressed point cloud, point cloud registration)

图像匹配，可以实现目标图像的匹配，匹配出最相似的训练图片。(Image matching, the matching can achieve the target image, matching the most similar training images.)