VCmeanshift

用Visual C++实现Hough变换(Using Visual C++ Achieve Hough Transform)

用openCV实现，用Camshift算法实现彩色目标跟踪(openCV,color object tracking usingCamshift)

MC算法实现CT医学图像的三维重建，面绘制技术(MC algorithm CT medical image reconstruction, surface rendering technology)

这个源代码是用来实现图形之间的基本运算的，包括求交集、并集、差集等等。而且图形的形状也可以任意选择，可以是圆、线段、点、多边形等等。(the source code is used to achieve the basic graphics operations, including for intersection and set difference, and so on. The shape and graphics can choose freely, it can be won, segment, points, polygons, and so on.)

实现bmp图像读入和写出，彩色图像灰度化，直方图均衡化(Implement bmp image read and write, color image graying, histogram equalization)

合成孔径雷达目标的特征提取及其识别技术。国防科大写的！(synthetic aperture radar target feature extraction and recognition technology. Written by which this university!)

先对图像进行DCT变化再进行水印嵌入，用于图像加密，保护版权，在版权保护方面有较大的用途。(Digital Watermarking DCTembed)

主成分分析PCA源代码（C++，matlab）主成分分析PCA源代码（C++，matlab）(Principal Component Analysis PCA source code (C++, Matlab), principal component analysis PCA source code (C++, Matlab))

说明：  MFC结合VTK做图像处理的程序，实现了图像的显示和缩放功能。(MFC combines VTK to do image processing procedures, to achieve an image display and the zoom feature.)

说明：  使用gpu、cpu并行进行sift算子计算匹配，能够在原来的基础上加速处理，但对显卡要求较高，具体环境配置使用方法可以参照mannual(SiftGPU is an implementation of SIFT [1] for GPU. SiftGPU processes pixels parallely to build Gaussian pyramids and detect DoG Keypoints. Based on GPU list generation[3], SiftGPU then uses a GPU/CPU mixed method to efficiently build compact keypoint lists. Finally keypoints are processed parallely to get their orientations and descriptors. SiftGPU is inspired by Andrea Vedaldi s sift++[2] and Sudipta N Sinha et al s GPU-SIFT[4] . Many parameters of sift++ ( for example, number of octaves, number of DOG levels, edge threshold, etc) are also available in SiftGPU. The shader programs are dynamically generated according to the parameters that user specified. SiftGPU also includes a GPU exhaustive/guided sift matcher SiftMatchGPU. It basically multiplies the descriptor matrix on GPU and find closest feature matches on GPU. Both GLSL and CUDA implementations are provided. )