Khan2

一种二维EMD算法，作者是Anna Linderhed，该学者博士研究课题为二维EMD和小波变换，她的二维EMD算法与Nenus或其他学者的算法有较大的不同。(A two-dimensional EMD algorithm, the author is Anna Linderhed, her Ph.D. research topic is bidimensional EMD and wavelet transform. Her two-dimensional EMD algorithm is quite different from that of Nenus and other scholars.)

在图像中加入椒盐噪声的源代码，编译后的可执行文件须带参数运行(the image Salt and Pepper to the noise source, the compiled executable file parameters required to bring Operation)

支持向量机的MATLAB源程序，效果真的很不错(SVM MATLAB source, the effect is really very good)

Image processing of band reject filter and archive particular bit of an 8 bit pixel of an image

镜片面形数据通过zernike多项式用协方差方法拟合得到的zernike系数(Zernike coefficients covariance fitting)

从一个模式识别toolbox中摘取的计算zernike不变矩的源代码，结果为图像的n阶m重zernike不变矩(From a pattern recognition toolbox extracted Zernike moment invariants calculation of the source code, the results of image re-n-order m Zernike moment invariants)

背景减除的完整算法，《A Statistical Approach for Real-time Robust Background Subtraction and Shadow Detection》作者源代码(The "src" folder contains all of the source code. To run the program, follow these simple steps: 1. Start MATLAB and ensure the "src" folder is your current working directory. 2. Edit "bs.m" and "classify.m" so that the file paths point to the proper directories as specified. 3. In the MATLAB environment type "bs" which will train the background model from the sequence you specified in (2). 4. In the MATLAB environment type "classify" which will classify the sequence of images you specified in (2). 5. Now the classified images should be saved to disk in the folder you specified in (2).)

基本实现bm3d算法，并且附有测试图片，希望对大家有所帮助(The basic implementation of bm3d algorithm, with the test picture attached, hope to help you.)

用kinect获取3D图像，并从3D图像中用OpenCV方法从深度图中获取梯度边缘，从而得到更多信息，做后续处理(Get with kinect 3D images, and get the edge gradient from the depth map from a 3D image using OpenCV method to obtain more information, do follow-up treatment)

说明：  对图像进行中值滤波，去除噪声，并与原图像进行比较(Median filter on the image to remove noise, compared with the original image)