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矩形信号的frft（分数傅里叶变换wigner_ville_1.m 单分量信号去噪 wigner_ville_2.m 双分量平行信号去噪 wigner_ville_3.m 3分量信号频率检测）分数傅里叶变换是经典傅里叶变换的另一种改进方式。它的主要特点是提供研究对象从时间域到频率域全过程的综合描述，随着阶数从0连续增长到1，分数傅里叶变换展示出研究对象从纯时间域逐步变化到纯频率域的所有变化特征。因此，分数傅里叶变换提供了远比傅里叶变换多得多的可供选择的数据处理和分析方法。(The rectangular signal FRFT (Fractional Fourier Transform) Score Fourier transform is another modification of the classic Fourier transform. Its main feature is a comprehensive description of the study from the time domain to the frequency domain, with the order of continuous growth from 0 to 1, the fractional Fourier transform demonstrate the object of study from a purely time-domain gradually change to a pure frequency domain variation. Thus, the fractional Fourier transform provides data processing and analysis methods than Fourier transform much more to choose from.)

数字散斑相关计算中，亚像素位移检测，梯度算法(sub-pixel, gradient method,DSCM)

水平集分割图像的一种算法，属于比较前沿的一种图像分割方法。(A level set image segmentation algorithm, a relatively cutting edge of a segmentation method.)

目标跟踪中常用到的阿尔法-贝塔滤波 有图有真相(Target Tracking commonly used to Alpha- Beta filtering map with the truth)

hit-miss方法下的图像骨架提取，很好用(hit-miss method of image skeleton extraction, very good use)

用水平集（level set）方法和mumford-shah图像分割模型的图像分割matlab源代码(Mumford functional for segmentation and level sets.)

实现retinex中的msr，关于雾天图像处理。(Msr in achieving retinex，Image processing on the fog)

生成静态散斑图，通过快速傅里叶变换，得到激光散斑图。(Generate static speckle pattern)

一个非常实用的las文件处理工具，可以实现很多功能，如读写las文件，分类，分块，抽稀，合并，生成边界线，生成DEM，点云高度归一化，数据格式转换，波形数据压缩等(A very useful the las file processing tools, you can achieve a lot of features, such as read and write las file classification block Rarefy merge to generate the boundary line, generated DEM point cloud height normalized data format conversion, waveform data compression, etc.)

斯坦福大学的姚邦鹏开发的一个图像分类算法，使用random forest实现了图像的精细区域描述，赢得了PASCAL VOC2011图像分类竞赛中的winner prize.经测试，程序完全可运行，且提供了MAC和Windows下的两种程序。(Image Classification: An Integration of Randomization and Discrimination in A Dense Feature Representation The goal of our method is to identify the discriminative fine-grained image region that distinguishes different classes. To achieve this goal we sample image regions from dense sampling space and use a random forest algorithm with discriminative classifier. Each node of the tree of random forest is trained and tested with fine-grained image patches combining the information from upstream nodes together. We implemented each node of the tree with a discriminative SVM classifier, which makes the node as a strong classifier. )