gathers

用sift算子对两幅图像进行特征点的自动搜索。(Sift operator with the image feature point english)

视频格式转换 这里主要是mpeg格式 转化为avi，yuv等多种格式(Video format conversion here is mainly converted to avi mpeg format)

压缩传感，MATLAB，信号重建，正交匹配追踪，打破乃奎斯特定理(Compressed sensing, MATLAB, signal reconstruction, orthogonal matching pursuit, breaking the Nyquist Theorem)

delaunay三角化程序 delaunay三角化程序 delaunay三角化程序(delaunay triangulation delaunay triangulation process procedures delaunay triangulation process)

通过帧差法来提取背景的程序，效果不错通过帧差法来提取背景的程序，效果不错‘(通过帧差法来提取背景的程序，效果不错通过帧差法来提取背景的程序，效果不错)

这是模式识别中线性分类器中最常用的fisher线性分类器，并在结果中画出了分类线和分类效果(This is image restoration in the Iterative Blind Deconvolution algorithm matlab source for the study of digital image has a lot of benefits!)

对最大类间方差法进行改良，引入最小类内方差，进一步提高分割效果(For improved Otsu method, introducing the minimum interclass variance, further improve the segmentation effect)

卡尔曼滤波源代码 Some brief notes ---------------- kfdemo.m is a Matlab script file to run a demonstration of the Bayesian Kalman filter. It loads file kfdemo.mat (saved as version 4 so that it will read in either v4 or v5 Matlab). The other files are called by kf.m (the Kalman filter) or included as they may be useful. Normalis.m normalises data to zero-mean, unit variance along components. I always do this before any further analysis. Steve Roberts 7-2-98 (Kalman filter source code Some brief notes kfdemo.m is a Matlab script file to run a demonstration of theBayesian Kalman filter. It loads file kfdemo.mat (saved as version 4so that it will read in either v4 or v5 Matlab). The other files are called by kf.m (the Kalman filter) or includedas they may be useful. Normalis.m normalises data to zero-mean, unit variance along components. I always do this before any furtheranalysis.Steve Roberts 7-2-98)

这是一个专门为菲涅尔衍射设计的一个仿真程序！参考光为平面波！本人测试过，没有问题存在！(This is a design specifically for the Fresnel diffraction of a simulation program! A plane wave reference beam! I tested, no problem!)

这是一个MATLAB工具箱包括32个降维程序，主要包括 pca,lda,MDS等十几个程序包，对于图像处理非常具有参考价值！！( ，This Matlab toolbox implements 32 techniques for dimensionality reduction. These techniques are all available through the COMPUTE_MAPPING function or trhough the GUI. The following techniques are available: - Principal Component Analysis ( PCA ) - Linear Discriminant Analysis ( LDA ) - Multidimensional scaling ( MDS ) - Probabilistic PCA ( ProbPCA ) - Factor analysis ( FactorAnalysis ) - Sammon mapping ( Sammon ) - Isomap ( Isomap ) - Landmark Isomap ( LandmarkIsomap ) - Locally Linear Embedding ( LLE ) - Laplacian Eigenmaps ( Laplacian ) - Hessian LLE ( HessianLLE ) - Local Tangent Space Alignment ( LTSA ) - Diffusion maps ( DiffusionMaps ) - Kernel PCA ( KernelPCA ) - Generalized Discriminant Analysis ( KernelLDA ) )