功能很全的车牌识别系 platenumber

This is simple calculator program. I m making a GUI calculator interface in MFC. this program works pretty well.

数字音频水印的嵌入，提取，然后进行攻击性实验，检验其鲁棒性(Digital audio watermark embedding and extraction, and then attack experiment to test its robustness)

读取图像，将RGB图像转化为灰度图像，显示并保存(The RGB image is transformed into gray image)

此程序利用openCV中人脸识别于模版匹配实现机器人的运动控制功能，其中机器人控制用了Aria的库和模拟器，使用者可根据控制需要替换这部分程序。 本程序的核心部分一直用于只能轮椅的人脸控制。(This process us face recognition in openCV and template matching to achieve robot motion control functions, which used the Aria library and robot simulator, user need replace this part of the control procedures to achieve their application.)

说明：  PCL+ MFC+ Visualization 点云显示(PCL+ MFC+ Visualization)

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.)

c++特征提取，提取图像当中的红色灯笼。。。(c++ feature extraction, image extraction among red lanterns)

图像亮度和对比度调节，已验证，分别有亮度调节和对比度调节的调整框(Image brightness and contrast adjustment, has been verified, respectively, brightness adjustment and contrast ad)

实现凸多边形排样，使用临界多边形NFP判断两个凸多边形形是否相交,使用启发式算法提高效率， (Convex polygon nesting, polygon NFP to determine whether two convex polygon-shaped intersection, the use of heuristic algorithms to improve efficiency,)

本程序为图像锐化的经典程序，源码里有详细的标注。(the procedures for sharpening images of the classic procedures are detailed source of tagging.)