L2

Hilditch算法是现有的一种细化算法,它适用于 输入图像为0和1的二值图像。像素为1的区 域是需要细化的部分, 像素为0 的区域是背景(Hilditch thinning algorithm is an existing algorithm, it applies to the input image is 0 and 1 of the binary image. Pixel region 1 is a need to refine the part of the pixel area is the background for the 0)

使用规定分布生成空间随机噪声，可以选择噪声类型和参数。(Using the prescribed distribution to generate spatial random noise, we can choose noise types and parameters.)

说明：  PWLS算法主要用于CT重建前的投影数据处理，能够很好的用于CT迭代算法中，对图像进行去噪(PWLS algorithm is mainly used for projection data processing before CT reconstruction. It can be well used in CT iterative algorithm to denoise images.)

通过matlab实现对视频的实时提取及处理(Real-time extraction and processing of the video by matlab)

这是一种很强的人脸检测程序，你可以试试，但我也不知是用什么方法。(This is a very strong face detection process, you can try, but I do not know in what ways.)

雷达信号处理基础教程，对雷达信号基础处理技术提供了简洁、统一、现代的表述，包括信号模型、匹配滤波、脉冲压缩、多普勒处理，阈值检测等。(Fundamentals of Radar Signal Processing)

一个去水印的程序，已调好。里边包含实例图片，可以帮助大家理解这个代码。(A program to watermark has a good tune. Inside contains examples of pictures that can help us to understand the code.)

下面给出了48项泽尼克多项式，外加一项常数项。需要注意的是，读者并不需要严格按照下文所示的顺序排列这些泽尼克项，实际上在不同的应用和机构会采用不同的排列顺序。 表中的#0项是个常数或者说是平移项（piston term），这一项的系数也代表了平均光程差；而#1和#2项分别是x和y方向的倾斜项（tilt terms），#3代表了聚焦，因此，#1到#3项代表了波前的高斯或者近轴特性；#4和#5项代表了像散和离焦，#6和#7项代表彗差和倾斜，而#8项代表了3级像差和离焦，也就是说#4到#8项为3级相差项；同样地，#9到#15项代表了5级像差，而#16到#24项代表了7级像差，#25到#35项代表了9级像差，#36到#48项代表了11级像差。(Here are 48 Zernike polynomials, plus a constant term. It is important to note that the reader does not need to strictly arrange the Zernike entries in the order shown below, but in fact different orders of arrangement are applied to different applications and institutions. Table #0 is a constant or a shift (piston term), a coefficient of this term also represents the average optical path difference; while #1 and #2 are inclined X and Y direction (tilt terms), #3 represents the focus, therefore, #1 to #3 on behalf of the wavefront. Gauss or paraxial properties; #4 and #5 represent the astigmatism and defocus, #6 and #7 represent coma and tilt, and #8 represents the 3 order aberration and defocus, that is to say #4 to #8 is 3 level difference; similarly, #9 to #15 on behalf of the 5 order aberration #16 and #24, to represent the 7 level #25 to #35 aberration, on behalf of the 9 #36 to #48 aberration, on behalf of the 11 order aberration.)

说明：  多种边缘检测算子对比 不需要改动 有sobel prewittts canny log(Multiple edge detection operators)

一种基于半监督的svm的图像分类方法。该方法通过聚类核的方法利用无标记样本局部正则化训练核的表达式。这种方法通过图像直接学习一个自适应的核。该程序仿真的是文章：Semi-supervised Remote Sensing Image Classification with Cluster Kernels。大家可以参考下。(A semi-supervised SVM is presented for the classification of remote sensing images. The method exploits the wealth of unlabeled samples for regularizing the training kernel representation locally by means of cluster kernels. The method learns a suitable kernel directly from the image, and thus avoids assuming a priori signal relations by using a predefined kernel structure. Good results are obtained in image classification examples when few labeled samples are available. The method scales almost linearly with the number of unlabeled samples and provides out-of-sample predictionsds)