基于马尔可夫随机场的图像恢复，给出产生MRF的程序并应用于图像去噪

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.)

实现了遥感图像可以进行阈值分割和分水岭分割(Threshold segmentation, watershed segmentation)

计算几何(C描述)第二版教材的配套程序,用C语言实现了本书的主要算法.(Computational geometry (C described) in the second edition of materials matching procedures, using C language to achieve the book)

说明：  一个图像变换程序，检测人脸，并剪切出需要的部分(An image transformation program, change the pixel size, cut out part of the required)

利用压缩感知实现波达方向估计，运用奇异值分解对接收信号进行降维，再利用L1范数进行估计(Using compressed sensing DOA estimation using singular value decomposition to reduce the dimensionality of the received signal, and then using the L1 norm estimate)

This is a Scaling function definition Image cutoff ratio for H = 3

图像剪切后，转换为LAB和HSV颜色空间，计算每个颜色通道的颜色矩，最后将所有变量整合到一个矩阵中。(after image cropping, convert the image into LAB nd HSV color space, then computing the color moments of each channel. finally combine this variables into one matrix.)

该代码为图像增强代码。用于光学或SAR图像增强。具有较好的效果。(The code for image enhancement Used for optical or SAR image enhancement has a good effect )

斯坦福大学的姚邦鹏开发的一个图像分类算法，使用random forest实现了图像的精细区域描述，赢得了PASCAL VOC2011图像分类竞赛中的winner prize.经测试，程序完全可运行，且提供了MAC和Windows下的两种程序。(Image Classification: An Integration of Randomization and Discrimination in A Dense Feature Representation The goal of our method is to identify the discriminative fine-grained image region that distinguishes different classes. To achieve this goal we sample image regions from dense sampling space and use a random forest algorithm with discriminative classifier. Each node of the tree of random forest is trained and tested with fine-grained image patches combining the information from upstream nodes together. We implemented each node of the tree with a discriminative SVM classifier, which makes the node as a strong classifier. )

偏最小二乘线性判别分析， 以及变量选择方法(partial least squares-linear discriminant analysis)