cdd

图像的反卷积 去卷积复原 效果还不错，欢迎下载(image deconvolution image restoration)

CCD 摄像机内外部参数快速准确的标定方法(Fast and accurate calibration method for internal and external parameters of CCD camera)

中国科学院长春光学精密机械与物理研究所 基于外极线约束条件的视频判读方法(Changchun Institute of Optics, Fine Mechanics and Physics epipolar constraint based on the video interpretation method)

基于主像素的背景建模方法。该方法对密集目标检测效果较好。(Based on the background of the main pixel modeling method. The method of intensive better target detection.)

包括基于SIFTGPU的sift特征提取，sift匹配，基于ransac误匹配点剔除等(SIFTGPU based sift feature extraction, sift matching, mismatching points based ransac removed, etc.)

基于visual c++平台，相机标定方法中的直接线性变换方法，即DLT方法(Based on visual c++ platform, camera calibration method of direct linear transformation method, namely DLT method)

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)

用VC实现Canny算子提取图像边缘，提高图像质量(Using VC realize Canny operator extracts image edge to improving image quality.)

此程序采用vc++6.0编写,可以实现4路视频采集,压缩以及编码(编码算法包括mpeg2),采用的采集卡是天敏vc4000,利用回调函数作为采集的主要手段 另外提供网络传输的服务端程序(This program uses a vc + +6.0 preparation, can achieve 4-channel video capture, compression and encoding (encoding algorithm including mpeg2), using capture card is Mortimer vc4000, use the callback function as the primary means of collection additionally provides end network transport services program)

程序设计步骤： 1、去均值 2、计算协方差矩阵及其特征值和特征向量 3、计算协方差矩阵的特征值大于阈值的个数 4、降序排列特征值 5、去掉较小的特征值 6、去掉较大的特征值(一般没有这一步） 7、合并选择的特征值 8、选择相应的特征值和特征向量 9、计算白化矩阵 10、提取主分量(Programming steps: 1 to mean 2, calculation of covariance matrix and its eigenvalues and eigenvectors 3, the calculation of covariance matrix eigenvalues greater than the threshold number of 4, in descending order of eigenvalue 5, remove the smaller eigenvalue 6, remove the larger eigenvalue (usually not this step) 7, the combined choice of eigenvalues 8, select the appropriate eigenvalues and eigenvectors 9, computing whitening matrix 10, principal component extraction)