搜索资源列表
BasicSnake_version2e
- The basic snake segmentation (2d and 3d). A snake is an active (moving) contour, in which the points are attracted by edges and other boundaries. To keep the contour smooth, an membrame and thin plate energy is used as regularization.
tv_reg
- This MATLAB .m file takes a "true" image, and an "observed" image that is the true image that has been noised with uncorrelated Gaussian noise. The function then performs Tikhonov regularization to clean up the defects in the image, with the Tikhonov
imageshift-and-opticalflow
- opticalflow.m determines the velocity shift between two given images - ideally they should be virtually identical, but there is an affected translation between the two. imageshift.m creates a straightforward translation effect on an image - edge
plotkoch
- Koch曲线是通过图形迭代的方式产生的，其迭代规则是：对一条线段，首先将它分成三等份，然后将中间的一份替换成以此为底边的等边三角形的另外两条边。无限次迭代下去，最终形成的曲线就是Koch曲线。-Koch curve is generated by way of graphical iteration, and its iteration rule is: for a segment, it is divided into three equal parts first, and then rep
main
- 边缘提取canny算子，用于提取图像的强边缘-Canny edge detection operator to extract the strong edges of the image
Cpp1
- ）根据校园图设计带权图，图的顶点表示各地点，存放地点名称、代码、简介等信息；图的边表示路径，存放路径长度等相关信息。（校园图能手动输入或者文件导入） 2）提供任意地点相关信息的查询。 3）提供任意地点的问路查询，即查询任意两个景点之间的一条最短的简单路径。 4）提供多个地点的最佳访问路线查询，即求途经这些地点的最佳（短）路径。 -) According to the campus map design a weighted graph, the vertex that locat
edgedet
- used in the detection of edges in the image given
Kruskal
- 采用Kruskal算法求最小生成树主要数据结构 edgeset GE 存放图中的所有边 int n,int e 存放图中的顶点数与边数 edgeset C 存放生成树中的边 vexlist gv 图中结点的顶点值 adjmatrix s 用来处理图中结点的查找与合并 int m1,m2 一条边上两顶点所属集合的序号 int k 最小生成树中的边数 int d //图中待扫描边元素的下标-For the Minimum Spanning Tree by Krusk
1-103
- 有向图，有向网，无向图，无向网的邻接表存储、增删顶点和边。-Directed graph, directed networks, undirected graph without adjacent to the network table to store, add and delete vertices and edges.
TV-demo
- Total Variation Denoising-TV denoising (scalar fidelity term) [ROF92] Reduces the total-variation of the image. Filters out noise while preserving edges. Textures and fine-scale details are also removed. In this demo the assumption is that a white
TSP00022304
- 基于广度优先搜索解决TSP问题，可以删除，添加边和节点-TSP based on breadth-first search to solve the problem, you can delete, add edges and nodes
Ford
- Bellman-ford算法，根据顶点和边的权值求出最短路径-Bellman-ford algorithm, based on the weights of vertices and edges of the shortest path obtained
Dijkstra
- 可以直接编译运行，输入点和边的信息，给出单源最短路径的结果。-Can directly compile and run, input points and edges information, presented the results of single-source shortest path.
Without-the-adjlink-to-map
- 从键盘输入无向图G的顶点个数v及边的个数e。建立有v个顶点、e条边构成的无向图G，采用邻接矩阵表示。V个顶点的值由键盘输入，元素类型为字符型。e条边的信息也由键盘输入。分别调用图的深度优先搜索、广度优先搜索图并输出相应的遍历序列。-Input from the keyboard without the number of vertices to the graph G v and the number of edges e. Establishment of a v vertices, e ed
Kruskal
- 编程实现Kruskal算法，生成最小代价生成树，其中利用最小堆算法实现。 （随机生成n个点，且随机生成k条边，形成连通图） 根据输入的顶点数的不同，分析时间复杂度。-Implement Kruskal’s algorithm based on min-heap and disjoint set data structure for constructing a minimum cost spanning tree. Generate weighted undirected com
Prim
- 编程实现Prim算法，基于最小堆数据结构，生成最小代价生成树。 （其中随机生成点和边，形成连通图） 根据输入的顶点数的不同，分析时间复杂度。-Implement Prim’s algorithms based on min-heap and disjoint set data structure for constructing a minimum cost spanning tree. Generate weighted undirected complete graph for
KrusklaPrim
- 最小生成树 Kruskal和Prim的比较 可以根据用户输入的点数和边数随机生成图并且保证是连通图-Kruskal and Prim' s minimum spanning tree can compare the number of points based on user input and the edges are randomly generated map and ensuring that connected graph
MaxFlow
- 最大流最小割 可以测试某一个特定的图 也可以随机生成n个顶点的图和k条边 用来分析时间复杂度-Maximum flow minimum cut to test a particular map can also be randomly generated graph of n vertices and k edges used to analyze the time complexity
M@-code
- program which includes the edge detection and is done inmatlab...helpful in finding edges in medical imges
GraphSeg
- This paper addresses the problem of segmenting an image into regions. We defi ne a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image. We then develop an effi cient segment