cs

isar成像处理，对测试数据进行处理的模板，可以显示二维像(ISAR imaging processing, the test data processing template, you can display the 2D image )

将原始载体图片经过一维冗余离散小波变换分成四个子频带，分别对每个子频带进行奇异值分解， 将水印图片灰度值直接按比例缩小嵌入到奇异值矩阵中，再将此矩阵进行一次奇异值分解得到奇异值当做此子带的奇异值， 重建此子带矩阵，而后将进行反冗余离散小波变换得到嵌入载体后的水印图像。(The original image after the one-dimensional vector redundant discrete wavelet transform is divided into four subbands, respectively, of each sub-band for the singular value decomposition, the watermark image gray value scale down directly embedded into the matrix singular value, then this matrix is ​ ​ a Singular value decomposition singular value as the singular values ​ ​ of this sub-band, the reconstruction of this sub-band matrix and after anti-redundant discrete wavelet transform watermark image after embedding the carrier.)

该程序采用总变差方法去除图像噪声，希望大家喜欢(the procedure used to remove the deteriorating image noise, hope you like)

可见光图像飞机目标识别 模糊差影法 改进自适应阈值分割法 七阶Hu不变距(aircraft recognition)

此程为目标检测算法程序，采用经典的光流算法，最后可以的到一个运动光流图。(This process procedures for the target detection algorithm, using the classical optical flow algorithm, and finally to a movement to the optical flow diagram.)

水平集(level set)的基本思想是将界面看成高一维空间中某一函数ψ（称为水平集函数）的零水平集，同时界面的演化也扩充到高一维的空间中。我们将水平集函数按照它所满足的发展方程进行演化或迭代，由于水平集函数不断进行演化，所以对应的零水平集也在不断变化，当水平集演化趋于平稳时，演化停止，得到界面形状。(The level set (level set) the basic idea is to interface as a higher dimensional space in a function (called a level set function) of the zero level set, and the interface evolution also extended to higher dimensional space. We evolve or iterate the level set function according to the evolution equation it satisfies. Because the level set function keeps evolving, so the corresponding zero level set is also changing. When the level set evolves to a stable state, the evolution stops, and the interface shape is obtained.)

matlab 车牌识别，含有gui界面，边缘检测车牌定位，神经网络字符识别(Matlab license plate recognition, including GUI interface, edge detection, license plate location, neural network, character recognition)

经典的K均值聚类，源码加利用三维高斯简单数据的实现对三类数据的聚类(Achieve the effect of classical K-means clustering, plus simple data)

BenchmarkDataset,做图像处理的标准图像库(BenchmarkDataset, do image processing standard image library)

说明：  按照最邻近原则把待分类样本点分到各个簇。然后按平均法重新计算各个簇的质心，从而确定新的簇心。一直迭代，直到簇心的移动距离小于某个给定的值。(According to the nearest neighbor principle, the sample points to be classified are divided into clusters. Then the centroid of each cluster is recalculated according to the average method to determine the new cluster center. Iterate until the moving distance of the cluster center is less than a given value)