PIVlab

在基于的图像分割方法中，CV模型是一个非常重要模型。这是其实现ＭＡＴＬＡＢ源代码。(Based image segmentation method, CV model is a very important model. This is the MATLAB source code for its implementation.)

SAR复数数据转换，RAW格式数据转换，雷达图像滤波，LEE-sigema滤波，Frost滤波，Gamma-MAP滤波，KUAN滤波，雷达图像几何处理，斜距到地距转换程序。(SAR complex data conversion, RAW format, data conversion, radar image filtering, LEE-sigema filter, Frost filter, Gamma-MAP filtering, KUAN filtering, radar image geometry processing, slant range to ground from the conversion process.)

人脸检测，能够检测出多张人脸，通过建立肤色模型，图像预处理等，分割出肤色区域，效果不错。(Face detection and can detect more than one face, through the establishment of skin color model, image preprocessing, segmentation of color of skin area, the effect is good. )

利用最大有效圆法或最大内切圆法提取掌纹掌脉图像的roi(Using maximum effective circle method or maximum inscribed circle method to extract palm print palm vein image ROI)

说明：  非局部去噪算法-nonlocal denoise 源码(Non-Local Denoising Algorithm-nonlocal denoise source)

很有用的gdal库，并且已经编译好。请多多指教!(It is a very good resource！Please download！！)

是图像处理中常用的测试图库，非常实用,如lena图片， 青椒图片，以及zelda图片等(is the test pictures used in image processing very useful)

将2D图像转换为红蓝3D效果。程序由本人编写，算法来自论文《A novel 2D-to-3D conversion technique based on relative height depth cue》by Yong Ju Jung等。(2D-to-3D Conversion. The codes are written by myself and according to the article "A novel 2D-to-3D conversion technique based on relative height depth cue" by Yong Ju Jung.)

实现稀疏表示算法，以lena图像为例，主要用ksvd和omp算法(The sparse representation algorithm is implemented. The Lena image is taken as an example, mainly using the ksvd and OMP algorithms.)

这个函数分配一个数据缓冲区与多个颜色指定系统上的乐队。这种类型的缓冲区允许表示彩色图像(例如,RGB)。 这个函数为每个指定分配缓冲区,一个二维表面颜色乐队。您可以使用MbufAlloc1d()和MbufAlloc2d()来创建单独的乐队一或二维 数据缓冲区,分别。 分配一个缓冲区后,我们建议您检查是否操作成功,使用MappGetError(),或通过验证,不是M_NULL返回的缓冲区的标识符。(This function allocates a data buffer with multiple color bands on the specified system. This type of buffer allows for the representation of color images (for example, RGB). This function allocates buffers that have a two-dimensional surface for each specified color band. You can use MbufAlloc1d() and MbufAlloc2d() to create single band one- or two-dimensional data buffers, respectively. After allocating a buffer, we recommend that you check if the operation was successful, using MappGetError(), or by verifying that the buffer identifier returned is not M_NULL.)