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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.)

BRISK特征点提取源代码，Linux及Windows，MATLAB环境。(Linux and Windows)

混合高斯(Mixture of Gaussian, MOG) 背景建模算法和Codebook 背景建模算法被广泛应用于监控视频的运动目标检测问题, 但混合高斯的球体模型通常假设RGB 三个分量是独立的, Codebook 的圆柱体模型假设背景像素值在圆柱体内均匀分布且背景亮度值变化方向指向坐标原点, 这些假设使得模型对背景的描述能力下降. 本文提出了一种椭球体背景模型, 该模型克服了混合高斯球体模型和Codebook 圆柱体模型假设的局限性, 同时利用主成分分析(Principal components analysis, PCA) 方法来刻画椭球体背景模型, 提出了一种基于主成分分析的Codebook 背景建模算法. 实验表明, 本文算法不仅能够更准确地描述背景像素值在RGB 空间中的分布特征, 而且具有良好的鲁棒性.(The background modeling algorithm of mixture of Gaussian (MOG) and codebook is widely used in moving object detection of surveillance video. However, the ball model of MOG usually assumes that the three components of RGB are independent, while the cylinder model of codebook assumes that the value of background pixel is distributed uniformly within the cylinder and the changing direction of brightness points to the origin of the coordinate system. These assumptions reduce the descriptive capability for background modeling. Therefore, the paper proposes an ellipsoid- based background model, which overcomes the MOG and codebook0s limitations. By using principal component analysis to depict the ellipsoid background model, a novel PCA-based codebook background modeling algorithm is proposed. Experiments show that this algorithm can not only give more accurate description of the distribution of background pixels but also have a better robustness.)

【实例简介】 通过调用海康sdk，按时间间隔进行拍照

基于MATLAB的鱼眼镜头几何矫正程序，将鱼眼镜头下产生的图像变换为正常的图像(Geometric correction procedures based on MATLAB fisheye, fisheye lens image to a normal image)

利用形态学的腐蚀运算提取图像边界 编写细化算法提取骨架的程序(Using morphology corrosion operation to extract image edgePreparation of thinning algorithm to extract the skeleton program)

这是冈萨雷斯版数字图像处理中的几个函数的实现，包括gmean,以及书中的例题5.7和5.10。(This is the realization Gonzalez version Several digital image processing functions, including gmean, and examples in the book of 5.7 and 5.10.)

利用PCA检测图像的复制粘贴区域。检测效果良好。(PCA was used to test digital image s copy and paste area.)

C# 动态绘制曲线

通过sift特征提取实现两幅图片无缝拼接(Seamless splicing of two images by SIFT feature extraction)