LFMCW

jpeg编码工程(YUV2RGB) 文件中含有说明文件,保证可以使用(jpeg encoding works (YUV2RGB) file contains the documentation to ensure that you can use)

canny边缘检测，还有车道线检测。效果还可以。(canny edge detection, and lane detection. The effect can be.)

里面涉及了线宽线型的设置，各种图形，还有画笔画刷设置选择，颜色设置等，代码非常齐全，还有编程的每一步都有截图和说明(Which involves the linewidth linear settings, a variety of graphics, and brush brush settings selection, color settings, the code is very complete, and programming of every step have screenshots and description)

利用k—mean算法根据U、V进行聚类分块，最后完成图像分割，分割出得块用不同的灰度等级表示出来。(K-mean algorithm to cluster sub-blocks according to the U, V, to finalize the image segmentation, segmentation have blocks with different gray levels represented.)

对数字图像的处理、灰度化等，对数字图像进行基本的操作与处理(Basic digital image manipulation and processing of digital image processing, gray,)

三维最大Renyi熵的灰度图像阈值分割算法(Maximum Renyi entropy of three-dimensional gray-scale image segmentation algorithm)

表面张力CSF模型，用于计算微重力条件下的两相流问题。(Comtinum Saface tension Force)

说明：  很好用的三维重建软件，VisualSFM软件允许上传一系列图像，它从这些图像中找到每一个图像的特定特征，利用这些特征信息重建出3D模型的稀疏点云，而后还可进行稠密点云重建。(The VisualSFM software, a good 3D reconstruction software, allows uploading a series of images. It finds the specific features of each image from these images, and USES these feature information to reconstruct the sparse point cloud of the 3D model, and then it can reconstruct the dense point cloud.)

一个用VC编的混沌图像加密程序，演示了如何读写图像，如何用混沌算法加密解密。(a program for encrypting image by chaos algorithm,based on VC.)

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.)