DPC

本系统中VIS欠缺的SIFT_VC.lib文件。。。 http://www.pudn.com/downloads224/sourcecode/math/detail1055031.html(This is lib file, which is used in Video Intelligent System (VIS) based on the Microsoft Visual Studio 2008 compiler environment and OpenCV 2.0 library. It includes foreground detection, motion object detection, motion object tracking, trajectories generation and analysis modules. It realizes a friendly interface based on dialog, which provides a convenient example for new learners. keywords: opencv, mixture of gaussian model, sift feature and ransac method, mean shift, particle filter, kalman filter, object detection and tracking, video intelligent system.)

MATLAB的一个源文件。用差分直方图位移的可逆数字水印实现图像的完整性认证。(MATLAB as a source file. Histogram using differential displacement reversible digital watermarking images realize the integrity of certification.)

各种图像处理技术举例。其中原代码可以直接使用，非常不错。(various image processing technology, for example. The original code which can be used directly, very good.)

基于AR2模型的粒子滤波源代码，可用于图像处理，视觉跟踪，希望可以帮助有需要的人(AR2 model particle filter based on source code can be used for image processing, visual tracking, I hope to help people in need)

分别用一阶、二阶以及Canny边缘检测算法进行图像边缘检测(Respectively first, second, and Canny edge detection algorithm for image edge detection)

说明：  文件有几个基于视网膜的图像增强技术，有SSR，MSR代码(There are several image enhancement technologies based on retina, including SSR and MSR codes.)

基于Retinex的MSRCR图像增强算法的源代码，已经测试，可以直接使用。(Based on the Retinex MSRCR image enhancement algorithm of source code, have been tested, can use directly )

讲解滤波与二值图像处理，比较简单的图像处理源码(Explain the filtering and binary image processing, image processing source code is relatively simple)

混合高斯模型使用K（基本为3到5个） 个高斯模型来表征图像中各个像素点的特征,在新一帧图像获得后更新混合高斯模型,用当前图像中的每个像素点与混合高斯模型匹配,如果成功则判定该点为背景点, 否则为前景点。通观整个高斯模型，他主要是有方差和均值两个参数决定，,对均值和方差的学习,采取不同的学习机制,将直接影响到模型的稳定性、精确性和收敛性。由于我们是对运动目标的背景提取建模，因此需要对高斯模型中方差和均值两个参数实时更新。为提高模型的学习能力,改进方法对均值和方差的更新采用不同的学习率 为提高在繁忙的场景下,大而慢的运动目标的检测效果,引入权值均值的概念,建立背景图像并实时更新,然后结合权值、权值均值和背景图像对像素点进行前景和背景的分类。(Gaussian mixture model using K (essentially 3-5) Gaussian model to characterize the features of each pixel in the image, in the image of the new frame for updated Gaussian mixture model, with each pixel in the image with a Gaussian mixture current model matching, if successful, determined that the point of the background points, otherwise the former attraction. Throughout the entire Gaussian model, he mainly has two parameters determine the variance and the mean, the mean and variance of the study, to take a different learning mechanism, will directly affect the stability, accuracy and convergence model. Since we are moving object extraction of the background modeling, so the need for the Gaussian model variance and mean two parameters real-time updates. In order to improve the learning ability of the model, an improved method for updating the mean and variance of different learning rates to improve in the busy scene, large and slow moving object detection results, the introduction of)

几种经典的图像平滑去噪算法程序，其中包括小波软阈值和硬阈值处理。还附送三个图像质量评价程序(several classic image denoising smoothing algorithm procedures, which include thresholding wavelet soft and hard threshold processing. Also attached three image quality evaluation procedures)