Description: 递归算法求一个有向图的强连通分量，输入格式如压缩包中data4.txt，第一行为顶点个数。输出到result.txt中。-recursive algorithm for a directed graph strongly connected component, the input format such as compression package data4.txt. the first acts of the number of vertices. Result.txt exported to China. Platform: |
Size: 6866 |
Author:王军 |
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Description: Computes "Connected Components" using Tarjan s Union-Find algorithm the result is returned in the same buffer as gray_level image with values equal to the label of the component. Platform: |
Size: 3072 |
Author:周平 |
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Description: 递归算法求一个有向图的强连通分量，输入格式如压缩包中data4.txt，第一行为顶点个数。输出到result.txt中。-recursive algorithm for a directed graph strongly connected component, the input format such as compression package data4.txt. the first acts of the number of vertices. Result.txt exported to China. Platform: |
Size: 7168 |
Author:王军 |
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Description: 求有向图的强连通分量，使用2次dfs的算法，具体名字我也忘掉了-Order to have a strongly connected directed graph component, the use of 2 dfs algorithm, the specific name I have forgotten Platform: |
Size: 2048 |
Author:zzq |
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Description: 顶点倒塌（Vertex Collapse）算法中，一开始图中的N个顶点看作N个孤立的超顶点(Super Vertex)，算法运行中，有边连通的超顶点相继合并，直到形成最后的整个连通分量。每个顶点属于且仅属于一个超顶点，超顶点中标号最小者称为该超顶点的根。-Collapsed vertex (Vertex Collapse) algorithm, one began to map the N vertices as N isolated vertices super (Super Vertex), algorithm, there are the super-edge-connected vertices have merged to form the final until the entire connected component . Each vertex belongs to and only belong to one super-vertex, super-vertex in the smallest numbers are referred to as the root of the super-vertex. Platform: |
Size: 2048 |
Author:chj |
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Description: java实现图像连通区域算法
*ImageLabel is an algorithm that applies Connected Component Labeling
*alogrithm to an input image. Only mono images are catered for.-*ImageLabel is an algorithm that applies Connected Component Labeling
*alogrithm to an input image. Only mono images are catered for. Platform: |
Size: 3072 |
Author:程磊 |
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Description: A rough implementation of "A Component-Labeling Algorithm Using Contour Tracing Technique" paper on an implementation of connected component algorithm using contour tracing, allowing the algorithm to do connected component labelling with just one pass through the image. Platform: |
Size: 1009664 |
Author:Hao Wooi Lim |
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Description: 二值图像连通域标记快速算法,能进行区域检测，附代说明文档-Binary image connected component labeling fast algorithm that can detect regional, with a generation of documentation Platform: |
Size: 47104 |
Author:lq8712b3 |
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Description: 二值图像连通域标记快速算法,能进行区域检测，附代说明文档，VB开发-Binary image connected component labeling fast algorithm that can detect regional, with a generation of documentation Platform: |
Size: 45056 |
Author:lq8712b3 |
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Description: 连通图程序源码,实验环境是Linux 欢迎下载-connected component labeling algorithm the environment of experiment is linux welcome to download Platform: |
Size: 6144 |
Author:jack |
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Description: 1. 用ＤＦＳ判断一个无向图是否是连通图；
2. 为有向图的边分类，将它们的边分为前向边、后向边和交叉边；
3. 用ＤＦＳ和点消除求有向图的拓扑排序；
4. 判断有向图是不是强连通图，若不是，求强连通分量；
5. 判断有向图是不是半连同图；
6. 判断有向图是不是单连通图；
7. 判断无向图是不是双连通图。
通过以上编程对ＤＦＳ的应用，进一步了解ＤＦＳ的算法及它所代表的算法思想。
-1. Using DFS to test if a given undirected graph is connected or not.
2. Classify the edges of a directed graph into tree edges, back edges, forward edges or cross edges by a depth-first traversal of the graph. If the given graph is undirected, classify the edges into tree edges and back edges. And verify if a directed or undirected graph has a cycle.
3. Compute the topological order of a directed graph using both DFS algorithm and source removal algorithm.
4. A strongly connected graph is a directed graph with every pair of vertices reachable from each other. A strongly connected component C of a directed graph G is a subset of maximal vertices such that every pair of vertices in the subset are reachable from each other. A strongly connected component graph GSCC of a graph G is a directed graph that each component C of G is considered as a single vertex in GSCC and there is an edge between components C1 and C2 if there exist an edge (u, v) in the graph G with u belongs to C1 and v Platform: |
Size: 10240 |
Author:卢竹江 |
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Description: Connected component labeling by an Iterative algorithm used for labeling image pixel.It will divide the region according to pixel value. Platform: |
Size: 10240 |
Author:Manish |
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Description: This contained BG/FG detection(simple version and adaptive background mixture models), blob tracking(connected component tracking and MSPF resolver, mean shift, particle filter), Kalman filter using OpenCV. It can be helpful who studying object detection and tracking algorithm. Also, It contain the video clip for testing algorithm.-This is contained BG/FG detection(simple version and adaptive background mixture models), blob tracking(connected component tracking and MSPF resolver, mean shift, particle filter), Kalman filter using OpenCV. It can be helpful who studying object detection and tracking algorithm. Also, It contain the video clip for testing algorithm. Platform: |
Size: 52594688 |
Author:byunghee |
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Description: The Strongly connected component algorithm. This is my exercise of Introduction to Algorithm subject. Platform: |
Size: 58368 |
Author:william |
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Description: 这是一个使用vc++实现的图像连通区域标记算法，可以找出二值化图像的连通域个数。-This is achieved using vc++ image connected component labeling algorithm can find the binary image of the number of connected domains. Platform: |
Size: 173056 |
Author:王帅 |
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Description: 这里列举二值图像连通域标记算法包括直接扫描标记算法和二值图像连通域标记快速算法-Here are the binary image connected component labeling algorithm including direct scan labeling algorithm and a binary image connected component labeling fast algorithm Platform: |
Size: 66560 |
Author:宁静 |
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Description: The basic steps of the connected-component text extraction algorithm are given below,
and diagrammed in Figure 10. The details are discussed in the following sections.
1. Convert the input image to YUV color space. The luminance(Y) value is used for
further processing. The output is a gray image.
2. Convert the gray image to an edge image.
3. Compute the horizontal and vertical projection profiles of candidate text regions
using a histogram with an appropriate threshold value.
4. Use geometric properties of text such as width to height ratio of characters to
eliminate possible non-text regions.
5. Binarize the edge image enhancing only the text regions against a plain black
background.
6. Create the Gap Image (as explained in the next section) using the gap-filling
process and use this as a reference to further eliminate non-text regions the
output.
-The basic steps of the connected-component text extraction algorithm are given below,
and diagrammed in Figure 10. The details are discussed in the following sections.
1. Convert the input image to YUV color space. The luminance(Y) value is used for
further processing. The output is a gray image.
2. Convert the gray image to an edge image.
3. Compute the horizontal and vertical projection profiles of candidate text regions
using a histogram with an appropriate threshold value.
4. Use geometric properties of text such as width to height ratio of characters to
eliminate possible non-text regions.
5. Binarize the edge image enhancing only the text regions against a plain black
background.
6. Create the Gap Image (as explained in the next section) using the gap-filling
process and use this as a reference to further eliminate non-text regions the
output.
Platform: |
Size: 41984 |
Author:Lee Kurian |
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Search - connected component algorithm