level-set

matlab区域增长图像分割程序，通过种子点搜索邻域的方法进行增长(matlab image segmentation procedures for regional growth)

VC++图像平滑处理+程序源码，实现图象的平滑（去噪声），锐化功能，测试时请在C盘下放一个测试用的Bmp图片，路径：C: est.bmp，没有的话没法测试。如上图所示是水平平滑处理，可以对比下，不过感觉颜色失真了，有兴趣的看一下。命令行编译过程如下： 　　vcvars32 　　rc bmp.rc 　　cl smooth.c bmp.res user32.lib gdi32.lib(VC++ image smoothing+ program source code, to achieve a smooth image (to noise), sharpening function in the C drive when testing a test of decentralization Bmp picture, path: C: test.bmp, no words can not test . Is shown above the level of smoothing, you can compare the next, but the feeling of color distortion, interested look. Command-line compiler as follows: vcvars32 rc bmp.rc cl smooth.c bmp.res user32.lib gdi32.lib)

MSE，最小均方误差,用于图像匹配，可用，亲测(MSE, minimum mean square error for image matching, available, pro-test)

基于核的相关滤波跟踪方法，最新的，TPMAI上的文章，速度超级快！(This MATLAB code implements a simple tracking pipeline based on the Kernelized Correlation Filter (KCF), and Dual Correlation Filter (DCF).)

说明：  使用MATLAB生成二维voronoi图的代码，可以用作后续二维voronoi的多晶模型建立(The code of two-dimensional Voronoi diagram generated by MATLAB can be used to build the polycrystalline model of two-dimensional voronoi.)

说明：  讲述PS技术的抠图经典问题，帮助你快速熟悉抠图的精华！(PS about the classic problem of technology, matting, matting to help you quickly familiar with the essence!)

该代码描述了一种用Gar小波进行纹理特征提取方法，可用于图像分析、视频处理、以及模式识别前期建立模式向量。(The code describes a texture with Gar wavelet feature extraction methods can be used for image analysis, video processing, and pattern recognition pre-established pattern vector.)

说明：  实现 %1、利用RGB模型识别天空区域；2、利用边缘识别检测天空边缘分割线；3、利用预测边缘点和实际边缘点的周围区域的均值滤波与本边缘点下方的像素之间的差异大小来修正天空边缘分割线 %4、将sky_seg+depthmap_v2中多余的带注释的地方删掉了 %！！！记得在295行，一定要改动是用I6还是I7，如果用sky_initia(f = edge(double(BW),'canny',thresh,sigma);)

说明：  这个源程序的主要功能是实现图像基于拉普拉斯金字塔的图像融合(The primary function of the source image is Laplacian pyramid-based image fusion)

压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为SP算法，对256*256的lena图处理，比较原图和SP算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间(Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix and SP algorithm as the reconstruction algorithm. Compare lena figure and the reconstruction results using SP algorithm at different sampling ratio (0.74,0.50.3),then each runs 50 times, compare the performance of PSNR and each running time)