EMDN

[SIAM]Nonlocal Active Contours该文件包含论文及代码，主要内容是在水平集图像分割中引入non-local的性质，构造能量泛函，进行图像分割(this file include both paper and code, the main idea is utilize the non-local peperty in level set method for image segmentation )

基于互相关的图像配准程序，能实现亚像素级的配准(Sub pixel level registration based on cross correlation image registration process)

输入一个带权无向图的顶点数、边数、各条边信息（两个顶点和权值），建立该图的邻接矩阵结构，输出该邻接矩阵。将上述无向图邻接矩阵转换为邻接表结构，输出该邻接表；根据该邻接表对无向图进行深度优先遍历序列和广度优先遍历序列，并输出遍历结果；用prim算法实现构造该带权无向图的最小生成树，并将该最小生成树的各条边信息输出。(Enter a weighted undirected graph with vertices, edges, each of the side information (two vertices and weights), the establishment of the adjacency matrix structure, the output of the adjacency matrix. The above undirected graph adjacency matrix into adjacency list structure, the output of the adjacency list according to the adjacency table undirected graph depth-first traversal sequence and breadth-first traversal sequence, and outputs through the results using prim algorithm construct the weighted undirected graph minimum spanning tree, and the minimum spanning tree information on respective sides of the output.)

对指纹进行识别，通过gabor进行指纹增强，然后细化得到结果(Fingerprints were identified and enhanced with gabor, and the results were refined)

从串口实时采集数据，读取家速度传感器的值，并且显示波形(Real-time collection of data from the serial port, read the value of home speed sensor, and display the waveform)

软硬阈值及软硬阈值折中法对bumps信号去噪，另外包含信噪比以及均方误差的求解。(Soft and hard threshold and soft and hard threshold compromise the bumps signal denoising, the other contains the signal to noise ratio and mean square error to solve.)

基于阴影形成的原理及阴影区域的特点，研究阴影区域的特征表示方法； 基于阴影区域特征设计或改进阴影检测方法，检测出户外图像中的阴影区域； 在检测出阴影区域的基础上，研究阴影去除方法； 编程实现相关方法并进行实验验证及分析。(Shadow detection and removal of single image)

采用相邻一定距离的邻域图像差，提高了抗干扰能力，对亮度变化不敏感(Matching based on neighborhood difference feature)

给图像加椒盐和高斯噪声，以及空域滤波（中值和均值滤波）！(to Image pretzels and Gaussian noise, and the spatial filter (mean values and filter)!)

用于图像拷贝检测的图像匹配，提取图像的SIFT特征点，构成图像的特征。(For image copy detection of image matching, image SIFT feature point extraction, and then sift feature point the image characteristics)