retinex

说明：  一个简单的画图程序，提供基本图元点、线、面，图元填充、裁剪的操作(Simple Draw)

方便实现图像的 处理，功能强大的算法文件，可以较好的实现图像的处理(Facilitate the realization of image processing, powerful algorithm files, you can better realize the image processing)

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. )

运动目标的给总与识别，可以实时显示目标的运动轨迹，支持高速相机实时处理视频数据，并可以获得运动目标的轮廓矩等信息(Moving target to the total and the identification of moving targets in real-time display track, real-time processing to support high-speed video camera and moving target access to information such as contour moments)

图像匹配程序，原理清晰，算法明确，功能全面(Image matching procedures, the principle of clarity, the algorithm clear, full-featured)

运动目标检测与跟踪，包括目标定位，匹配与跟踪!(moving target detection and tracking, including Target, matching and tracking!)

基于区域的稠密立体匹配方法（论文） 关键词：种子点；区域增长；稠密匹配(Based on Region Dense Three—dimensional Match Method)

用kinect获取3D图像，并从3D图像中用OpenCV方法从深度图中获取梯度边缘，从而得到更多信息，做后续处理(Get with kinect 3D images, and get the edge gradient from the depth map from a 3D image using OpenCV method to obtain more information, do follow-up treatment)

YUV Quantization using VS2010

实现对酒瓶子的边缘检测，背景差分，垂直和水平投影等，并可实现计数功能。(Achieve the bottles of edge detection, background difference, the vertical and horizontal projection, and to achieve counting.)