resample

说明：  随机抽样一致算法RANSAC，在图像配准中可以用于去除不精确的匹配点。(Random sample consensus algorithm RANSAC, in the image registration can be used to remove inaccurate matching point.)

采用beamlet变换方法，实现图像线特征提取，以此代码为基础的文章已被核心期刊录用(Beamlet transform methods used to achieve the image line feature extraction, this code is based on the core of the article has been hired Journals)

SVT 低秩矩阵恢复，可用于稀疏表示、图像恢复(SVT for low rank matrix completion)

说明：  图像配准，利用互信息进行图像配准，主要针对旋转和平移(Image registration using mutual information for image registration, the main response to rotation and translation)

说明：  一个立体视觉匹配程序，采用matlab编写(A stereo vision matching program, prepared using matlab)

matlab的GUI设计教程，希望对利用matlab设计GUI的用户带来一定的帮助(Matlab GUI design tutorial, I hope the use of MATLAB design GUI users to bring some help)

说明：  左右圆偏振光的推导公式，与处理结果。传统意义上根据界面曲率的不同，对于确定的透镜，其正极性或负极性是确定了的。但根据等离子体天线方向角的改变，透镜的部分极性也随之改变，换言之，同一透镜中正负极性均存在且可控制。(Derivation formula and processing results of left and right circularly polarized light)

music测向技术，快拍数，角度，频率，基本仿真(MUSIC algorithm direction finding technology)

moire条纹图像处理程序，由matlab开发的实例。(Moire fringe image processing procedures developed by the matlab example.)

主成分分析 （ Principal Component Analysis ， PCA ）或者主元分析。是一种掌握事物主要矛盾的统计分析方法，它可以从多元事物中解析出主要影响因素，揭示事物的本质，简化复杂的问题。计算主成分的目的是将高维数据投影到较低维空间。给定 n 个变量的 m 个观察值，形成一个 n ′ m 的数据矩阵， n 通常比较大。对于一个由多个变量描述的复杂事物，人们难以认识，那么是否可以抓住事物主要方面进行重点分析呢？如果事物的主要方面刚好体现在几个主要变量上，我们只需要将这几个变量分离出来，进行详细分析。但是，在一般情况下，并不能直接找出这样的关键变量。这时我们可以用原有变量的线性组合来表示事物的主要方面， PCA 就是这样一种分析方法。(Principal component analysis (Principal Component Analysis, PCA) or PCA. Is a statistical method to grasp the principal contradiction of things, it can be resolved diverse things out the main factors, revealing the essence of things, simplifying complex problems. The purpose of calculating the main component of high-dimensional data is projected to a lower dimensional space. Given n variables of m observations, forming an n ' m of the data matrix, n is usually large. For a complex matters described by several variables, it is difficult to know, so if you can grab something to focus on key aspects of analysis? If the main aspects of things just reflected on several key variables, we only need to separate out these few variables, for detailed analysis. However, in general, does not directly identify this critical variables. Then we can represent the major aspects of things with a linear combination of the original variables, PCA is one such analysis.)