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N年前上数字图像课时写的实验报告和程序，用滤波反投影（Filter Back Project，FBP)法重建图像。(Report and source code for data image restore experiment, using FBP.)

This a very useful program from a book. It is mainly used for face-detecting.It also includes interface-program.

说明：  在VC++下基于MRF来进行的图像分割源代码(In VC++ For MRF-based image segmentation source code)

人民邮电出版社，《VC++数字图像处理典型算法及实现》-- 第9章图像分割。 包括图像的并行边界分割、串行边界分割、并行区域分割、串行区域分割、Canny算子的实现等源代码(Posts )

用halcon编写的代码实例，用来给初学者学习(Use halcon code examples, for beginners learning )

基于meanshift/camshift的视频运动目标跟踪实现，通过鼠标手动选择跟踪目标。基于HSV空间模型，可手动调节H S V的阈值，显示前景、背景、反响投影图。开发工具是VS2008、OPENCV.(Based meanshift/camshift video moving target tracking achieved manually using the mouse to select the target track. HSV space model, you can manually adjust the threshold HSV, showing foreground, background, reaction projection. Development tools are VS2008, OPENCV)

k均值分类在遥感影像中的应用，完整的c++代码，注释详细易懂，并且已经验证过(k-means classification in remote sensing images in the application, complete c++ code, comments, detailed and easy to understand, and have been verified)

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.)

模式识别的Sobel和Kirsch边缘检测程序(Sobel and Kirsch edge detection algorithm of Pattern recognition)

基于OpenCV实现的图像剪切，截取小软件，对学习OpenCV的朋友比较有帮助(OpenCV based on the realization of the image shear, the interception of a small software, to learning more OpenCV friends help)