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一个很好类子来说明类模板的使用，可以比较明了类模板的使用方法，最好进行单步调试(use of class template )

VC++图像处理程序设计(杨淑莹)第四章_灰度变换源码(VC++ image processing program design (Yang Shuying) Chapter _ gray transform source)

生成avi视频截图序列。设置截图时长，截图频率，截图类型的参数。(Generate avi video sampling sequence.Screenshot duration, frequency and types of parameters are allowed to set freedom.)

傅里叶时域频域变换图形处理S参数在时域的应用(Fourier time domain frequency domain transform graphics processing S-parameters in the application of the time-domain)

一个不错的Meanshift算法流程图 初学者可以看看(failed to translate)

我写的贝叶斯fusion的Visual C+++源码，简明易懂，提供参数说明 可直接使用。 (The Bayesian fusion the Visual C+++ of what I wrote source, easy-to-understand parameters can be used directly.)

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.)

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.)

傅里叶变换及其逆变换的基于VC6.0实现(Fourier transform and its inverse transform)

数字图像处理实验——直方图的均衡化，打开图像并实现其均衡化(Digital image processing experiments- histogram equalization, open the image and realize its equalization)