Hough-transform

基于自动种子选择的区域生长法，克服了传统区域法的很多缺点(Region growing method based on automatic seed selection, to overcome the many shortcomings of the traditional regional law)

各种图像处理算法集合，包括灰度、滤波等，已调试，可用。(Collection of a variety of image processing algorithms, including grayscale, filtering, etc.)

动态的模拟天空中的云效果，速度特别流畅。(Dynamic simulation of the effect of cloud in the sky, the speed is very smooth.)

基于PCL的估算散乱点云的法矢量，并进行颜色渲染显示(estimation of normal vectors for point clouds and color rendering display based on PCL)

这是本智能图像处理的书教你如何读取BMP图像二值化中值滤波还有灰度变换图像匹配，检索(This is the intelligent image processing book teaches you how to read the BMP image binarization there are gray-scale transformation in the value of the filter image matching, retrieval)

说明：  用C语言实现图像的平滑，锐化及双线性内插算法的实现，效果很好(Using C language to achieve image smoothing, sharpening and bilinear interpolation algorithm to achieve good results)

VC做的图像处理方面的一些代码，仅供参考(VC do image processing of some code, only supplies the reference )

双目视觉经验总结，希望后面的不要犯错误。(Binocular visual experience summary, make no mistake behind.)

雷达扫描程序，图形界面中显示雷达的运动情况。程序简单。可以学习 (AppWizard has created this spp application for you. This application not only demonstrates the basics of using the Microsoft Foundation classes but is also a starting point for writing your OpenGl application. )

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.)