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图像处理,图像去雨滴、尘土，达到图像恢复的效果，还原图像。(image derain)

人脸检测：可自动检测人脸，速度还可以吧！(Face Detection : can automatically detect faces and can also speed!)

基于otsu的自适应阀值canny边缘检测(adaptive canny edge detection based on otsu)

数字图像处理源码,包含数字图像处理的诸如边缘检测等多个功能程序(C++)(Source digital image processing, including digital image processing such as edge detection and many other functional procedures (C++))

c++编写的一个简单的类似Photoshop的图像编辑系统(c++ written in a simple system similar to the Photoshop image editing)

图像处理方面，用VC++实现的连通区域标记的程序。(Image processing, with VC++ implementation of the procedures connected region markers.)

用VC编写的一个图像处理的程序：它可以实现图像的增强，包括：图像的平滑、中值滤波、梯度锐化、拉普拉斯锐化和伪彩色编码(VC prepared by an image processing procedures : it can enhance the images, including : image smoothing, Median filtering, sharpening gradient, Laplacian sharpening and pseudo-color coding)

先进行图像预处理，比如Hough变换，边沿检测，还有差影法，模板提取 ，识别，跟踪，最后是模板匹配。(First image preprocessing, such as the Hough transform, edge detection, as well as poor shadow method, template extraction, identification, tracking, and finally the template matching.)

1.打开左右两张影像2.Harris算法提取同名点3.显示提取同名点（若要进行下一步匹配则不建议显示，这里只是用于查看是否提取出）4.相关系数匹配（只在找出的同名点内匹配）5.LSM最小二乘匹配（先进行粗匹配，再精匹配）(1 Open the left and right images 2.Harris algorithm to extract two tie points 3 shows the extraction of corresponding points (not recommended to proceed to the next match show, here is used to see if extracted) 4. Correlation coefficient matching (just looking match within a point of the same name) 5.LSM least squares matching (rough first match, then fine match))

混合高斯(Mixture of Gaussian, MOG) 背景建模算法和Codebook 背景建模算法被广泛应用于监控视频的运动目标检测问题, 但混合高斯的球体模型通常假设RGB 三个分量是独立的, Codebook 的圆柱体模型假设背景像素值在圆柱体内均匀分布且背景亮度值变化方向指向坐标原点, 这些假设使得模型对背景的描述能力下降. 本文提出了一种椭球体背景模型, 该模型克服了混合高斯球体模型和Codebook 圆柱体模型假设的局限性, 同时利用主成分分析(Principal components analysis, PCA) 方法来刻画椭球体背景模型, 提出了一种基于主成分分析的Codebook 背景建模算法. 实验表明, 本文算法不仅能够更准确地描述背景像素值在RGB 空间中的分布特征, 而且具有良好的鲁棒性.(The background modeling algorithm of mixture of Gaussian (MOG) and codebook is widely used in moving object detection of surveillance video. However, the ball model of MOG usually assumes that the three components of RGB are independent, while the cylinder model of codebook assumes that the value of background pixel is distributed uniformly within the cylinder and the changing direction of brightness points to the origin of the coordinate system. These assumptions reduce the descriptive capability for background modeling. Therefore, the paper proposes an ellipsoid- based background model, which overcomes the MOG and codebook0s limitations. By using principal component analysis to depict the ellipsoid background model, a novel PCA-based codebook background modeling algorithm is proposed. Experiments show that this algorithm can not only give more accurate description of the distribution of background pixels but also have a better robustness.)