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在做图像处理时用遇到图像的阈值分割，这是几种图像的阈值分割方法(In doing image processing with experience image threshold segmentation, which is one of several image thresholding segmentation method)

说明：  光流场经典算法-LK算法，通过两幅图像计算光流场。(Lucas – Kanade Motion Estimation using Pyramids)

一个快速而精确的一阶稀疏图像复原算法。主要应用于图像去噪声。(A fast and accurate first-order sparse image restoration algorithm. Is mainly used in the image to the noise.)

visualc++6.0 实现图像中圆的检测(circle detection)

光流（运动估计）经典算法Horn and Schunck算法MATLAB程序(optical flow (Motion Estimation) classical algorithm Horn and Schunck algorithm MATLAB)

说明：  计算视频或者图像中相邻两帧的光流场。 本光流场算法使用是 多项式展开的方法 配置好视频的路径之后，直接运行就可以了。(Calculation of video or image of two adjacent optical flow field. The optical flow algorithm using the polynomial method is a good video of the path configuration, the direct operation of it.)

能够将图像坐标转化为世界坐标，根据图像像素修改参数(Can transform image coordinates into world coordinates, and modify parameters according to image pixels.)

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.)

利用多尺度高斯函数去除场景的光照分量；然后构造了一种二维伽马函数，利用光照分量的分布特性调整二维伽马函数的参数，降低光照过强区域的亮度值，提高光照过暗区域的亮度值，最终实现对光照不均匀图像的自适应校正处理。(The multi scale Gauss function is used to remove the illumination component of the scene, and then a two-dimensional gamma function is constructed. The parameters of the two-dimensional gamma function are adjusted by the distribution characteristics of the illumination component, the brightness value of the light illuminated region is reduced and the brightness value of the light dark area is improved. Finally, the adaptive correction of the uneven illumination image is realized. Positive treatment.)

基于sift的图片近似拷贝检测技术算法的实现(Sift-based image copy detection technology approximation algorithm)