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粒子滤波器使用示例，用于跟踪效果好，用VC++实现(example of particle filter)

利用OpenCV实现单幅图片对于手指的精确识别(Single image using OpenCV to achieve precise identification of the finger)

实现凸多边形排样，使用临界多边形NFP判断两个凸多边形形是否相交,使用启发式算法提高效率， (Convex polygon nesting, polygon NFP to determine whether two convex polygon-shaped intersection, the use of heuristic algorithms to improve efficiency,)

使用VB编写的图片反色源代码是图像处理的很好的教程(Image color VB source code)

图像球面化，对于输入的图片进行球面变换达到图形变形的效果(Spherical image of, for input images to spherical transformation to achieve the effect of graph distortion)

直接线性变换DLT，直接由像平面坐标到地面空间坐标的直接变换，可用于量测相机，非量测相机（本人用的是 高速相机）。配合前方交会验证l系数的准确与否。以DLT得到的结果为初值，用光束法平差可以期望得到更高精度。是数字摄影测量课程作业的例子。(Direct linear transformation DLT, and the direct conversion of coordinates directly from the image plane to the ground space coordinates, the camera can be used for measuring the non-measurement camera (I use a high-speed camera). Tie the forward intersection verify l coefficient accurate or not. To the the DLT results obtained for the initial value, bundle adjustment can expect greater accuracy. Examples of digital photogrammetry course work.)

一个经典的动态视频图像去隔行处理的源代码,能消除双场图像的场效应(A classic dynamic de-interlacing video processing source code, to eliminate dual-field image of the field-effect)

边沿检测与提取，轮廓跟踪的图象算法 用VC++实现，下载可以正常运行(Edge detection and extraction, contour tracing algorithm using image VC++ Realization can download the normal operation of)

根据D. Comaniciu, P. Meer: Mean Shift: A robust approach toward feature space analysis 以及 C. Christoudias, B. Georgescu, P. Meer: Synergism in low level vision.这两篇文献提供的方法编写的图像分割代码，作者是 Chris M. Christoudias, Bogdan Georgescu，代码经我看了后加了丰富的中文注释，希望可以给各位带来阅读上的方便。 基于meanshift的聚类分割方法包括滤波、区域融合等操作，通过调整sigma和sigmar来调整分割效果。(According to D. Comaniciu, P. Meer: Mean Shift: A robust approach toward feature space analysis, and C. Christoudias, B. Georgescu, P. Meer: Synergism in low level vision. These two documents prepared by the methods provided by image segmentation code , the author is Chris M. Christoudias, Bogdan Georgescu, after I read the code, add a rich Chinese notes, hoping to bring you the convenience of reading. Segmentation method based on clustering meanshift including filtering, regional integration and other operations, and by adjusting the sigma sigmar to adjust segmentation results.)

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.)