inexact_alm_rpca

用自相关法 来实现图像匹配，非常好用，值得一试(Autocorrelation method to achieve the image matching, very easy to use, it is worth a try)

指定图像部分，进行图片裁剪或者放单，类似于电商网站的图片放大镜(Specify the image part, cut or put out the picture, similar to the electricity supplier website picture magnifier)

说明：  改进LBP纹理提取以及纹理分类，主要包括三部分测试：纹理提取、分类和分割(Improved LBP texture extraction and texture classification, including three parts of testing: texture extraction, classification and segmentation)

在matlab开发环境中，使用模糊C均值聚类的方法对数据进行聚类(In the Matlab development environment, the use of fuzzy C-means clustering methods for data clustering)

对输入的一张彩色图像，自己写代码实现Harris Corner 检测算法： 1. 不能直接调用OpenCV 里面与Harris 角点检测相关的一些函数； 2. 只能用C/C++，不能用其他语言； 3. GUI 只能用自带的HighGUI，不能用QT 或其他的； 4. 平台可以用Windows， Linux, MacOS； 5. 显示中间的处理结果及最终的检测结果，包括最大特征值图，最小特征值图，R 图（可以考虑彩色 展示），原图上叠加检测结果等，并将这些中间结果都输出成图像文件； 6. 命令格式： “xxx.exe 图片文件 k 参数(=0.04) Aperture_size(=3)”。(The input of a color image, write your own code to achieve Harris Corner Detection Algorithm: 1. Can not be called directly inside OpenCV some functions associated with the Harris corner detection 2. To only use C/C++, can not be used in other languages 3. GUI only with their own HighGUI, can not use QT or other 4. The platform can be used Windows, Linux, MacOS 5. Display intermediate and final results of the test results, including the largest eigenvalues diagram, the minimum feature value graph , R chart (you can consider color display), picture superimposed on the test results, etc., and these intermediate results are output as an image file 6. Command Format: " xxx.exe picture file k parameters (= 0.04) Aperture_size (= 3 ). " )

说明：  利用BP算法进行SAR成像，BP算法是一种对时域回波数据进行成像的算法(BP algorithm was used for SAR imaging)

说明：  基于相位一致性的边缘检测，可使用该函数获取频谱图像的边缘(Edge detection based on phase consistency)

指纹识别算法，开源，十分有用，是matlab语言编写的，直接可以用再做修改(about matching，very useful )

说明：  基于高斯混合模型和隐马尔科夫模型的图像分割算法(Image segmentation algorithm based on Gaussian mixture model and hidden Markov model)

极化SAR图像中的将协方差矩阵数据进行PAULI分解的程序(Polarization SAR images will covariance matrix data Pauli decomposition process)