LBP

实现信号的离散傅立叶变换和反变换以及滤波，平滑处理(Realize signal discrete Fourier transform and inverse transform, as well as filtering, smoothing treatment)

TV模型用于图像修复，能够实现较好的图像修复效果，是基于偏微分方程的方法(TV model for image restoration, and to achieve better image restoration effect is a method based on partial differential equations)

说明：  实现手机拍摄图像的二维码图像的提取，包括QR码的旋转与校正，也可以用于其他条码，但是效果不是很好(The realization of mobile dimensional code image captured image extraction, rotation and including a QR code correction can also be used for other bar code, but the effect is not very good)

ASIFT图像特征提取源代码，有测试图片和demo(ASIFT image feature extraction source code, test images and demo)

在统计学中，皮尔逊积矩相关系数（英语：Pearson product-moment correlation coefficient，又称作 PPMCC或PCCs[1], 文章中常用r或Pearson s r表示）用于度量两个变量X和Y之间的相关（线性相关），其值介于-1与1之间。在自然科学领域中，该系数广泛用于度量两个变量之间的相关程度。它是由卡尔·皮尔逊从弗朗西斯·高尔顿在19世纪80年代提出的一个相似却又稍有不同的想法演变而来的。这个相关系数也称作“皮尔森相关系数r”。(In statistics, Pearson product-moment correlation coefficient (English: Pearson product-moment correlation coefficient, also known PPMCC or PCCs [1], the article used or Pearson s r r representation) is a measure of the two variables X and Y between correlation (linear correlation), a value between-1 and 1. In the natural sciences, the coefficient is widely used to measure the degree of correlation between the two variables. It is made a similar Karl Pearson Francis Galton in the 1880s but made a slightly different idea evolved. The correlation coefficient is also called Pearson s correlation coefficient r .)

该实例实现了 截图并识别截图中的文字功能 该项目需要在安装 Microsoft Office Document Imaging 下载地址 如下: http://www.xdowns.com/soft/4/25/2009/Soft_50716.html 

hough检测圆的Matlab源码！希望对大家有所帮助！(hough circle detection Matlab source code! Want to be helpful to everyone!)

光流法、帧间差分法、高斯背景模型差分法，在opencv环境下实现运动目标检测(moving objective detection matlab)

四步相移 多频外差 的matlab 理论演示(Four-step phase shift matlab theory demonstrates that multi-frequency heterodyne)

通过maketform和cp2form指令将倾斜图形通过放射变换和投影变换纠正过来(By maketform and cp2tform instructions diagonal pattern by affine transformation and projection transformation corrected)