Repair

该算法是局部线性嵌入的算法，代码无误，已运行，并且附带数据集，可以使用，非常实用。(The algorithm is locally linear embedding algorithm, the code is correct, has been running, and with the data set, you can use, very practical.)

说明：  采用霍夫曼技术实现压缩图像的重建，代码很详细，很不错。(Using Hoffman technology to achieve compressed image reconstruction, the code is very detailed, very good.)

说明：  《数字图像处理与机器视觉:Visual C++与Matlab实现》5 图像分割,霍夫变换,Hough变换直线检测的Matlab实现(&quot Digital image processing and machine vision: Visual C++ and Matlab to achieve&quot 5 image segmentation, Hough transform, Hough transform line detection of the Matlab implementation)

基于链码提取的傅立叶描述子,以及傅立叶反变换对图象边界进行重建 (chain code extraction based on Fourier descriptors, and the Fourier transform of image reconstruction border)

说明：  去除图像中的横向条纹，能有效恢复出原始图像(remove images of horizontal stripes, can be effectively restored from the original image)

对SAR图像分割 用马尔科夫随机长进行的。可以任意输入类别数目(For SAR image segmentation using Markov old airport)

实现了20000*20000以上的大tiff格式遥感图像的读写和显示，用于遥感图像处理(Achieve a 20,000* 20,000 or more large remote sensing image tiff format to read and write and display for remote sensing image processing)

在matlab环境下完成分水岭算法图像分割(Matlab environment in the completion of the watershed image segmentation algorithm)

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SL0算法，对256*256的lena图处理，比较原图和SL0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 SL0 algorithm as the reconstruction algorithm. Compare lena figure and the reconstruction results using SL0 algorithm at different sampling ratio (0.74,0.50.3),then each runs 50 times, compare the performance of PSNR and each running time)

说明：  对指定的一幅含有多个目标的灰度图像，进行基于阈值的图像分割处理，对结果图像进行基于数学形态学的后处理设计，以得到更理想的目标区域。最后对结果图像进行区域标记（以颜色区分不同区域）并提取目标个数、各目标区域的面积、圆形度等特征。(The gray image with multiple targets is segmented based on threshold, and the result image is designed based on mathematical morphology to get a better target area. Finally, we mark the region of the result image (distinguish different regions by color) and extract the number of targets, the area of each target region, roundness and other features.)