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一种最新的连通域标记方法,能够快速进行连通域标记-One of the latest connected component labeling method to quickly connected component labeling
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遍历一遍，判断图分为几部分（假定为P部分，即图有 P 个连通分量）,对于每一个连通分量，如果无环则只能是树，即：边数＝结点数－1,只要有一个满足边数>结点数－1,原图就有环.
-Traverse again, the determination map is divided into several parts (assumed to section P, with P in Figure connected component), for each connected compone
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二值化一幅图像，用不同彩色填充连通域（连通分量），在二值图像中找出轮廓并存储。-Binarized image, communicating with a different fill color field (connected component) to identify the outline of the binary image and stored.
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月亮和五毛钱,主要考查了广度优先搜索、强连通分量、缩点、动态拓扑规划等相关知识-Moon and five cents, the main test of breadth-first search, strongly connected component, shrinkage point, dynamic topology planning and other related knowledge
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八邻域实现的连通域标记，可以去除小的区域-Achieve eight neighborhood connected component labeling, you can remove some of the small non-candidate region
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强连通分支Kosaraju算法+源码.txt -Strongly connected component Kosaraj strongly connected branch Kosaraju algorithm+ source code. Txt strongly connected components of Kosaraju algorithm+ source code. Txtu algorithm+ source code. Txt
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本算法使用两种方式实现了连通域分析，二次扫描法和种子填充法。-this algorithm realizes the connected component analysis,including two methods: two pass and seed filling.
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Connected component used to connect the component
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在人脸局部图像中定位嘴的中心。MATLAB环境代码，配有实验用图。具体步骤：对输入图像进行二值化处理-标注二值图像中的连通分量-找出最大的连通分量-计算最大联通分量中心。《精通Matlab数字图像处理与识别》-The face part image is positioned in the center of the mouth. MATLAB environment code, with experimental Fig. Specific steps: input image binariz
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a good implementation of connected component extracting in c++ for document analysis.
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The growing size of a multiprocessor system increases its vulnerability to component
failures. In order to maintain the system’s high reliability, it is crucial to identify and
replace the faulty processors through testing, a process known as fau
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c++图像连通域标记，附带详细注释，有需要的下，相信会有所帮助-c++ image connected component labeling, with detailed notes, there is need for the next, I believe will be helpful
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找出最大的连通分量（嘴）
当前最大连通分量的大小
当前最大连通分量的索引-The largest connected component of the current index size of the largest connected component of the current find the largest connected component (mouth)
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Kruskal 算法是一种按照图中边的权值递增的顺序构造最小生成树的方法。其基本思想是：设无向连通网为G＝（V，E），令G 的最小生成树为T，其初态为T＝（V，{}），即开始时，最小生成树T 由图G 中的n 个顶点构成，顶点之间没有一条边，这样T 中各顶点各自构成一个连通分量。然后，按照边的权值由小到大的顺序，考察G 的边集E 中的各条边。若被考察的边的两个顶点属于T 的两个不同的连通分量，则将此边作为最小生成树的边加入到T 中，同时把两个连通分量连接为一个连通分量；若被考察边的两个顶点属于同一
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本资源介绍了一种高效的基于二值图像的连通域标记的算法-The resource based on a two value image connected component labeling algorithm is efficient
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.c++连通区域标记算法2，功能和matlab 中的 bwlabel相似，用opencv 编写-.c++ connected component labeling algorithm 2, function and MATLAB similar to the bwlabel, prepared by opencv
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This matlab program will detect the largest connected component
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各种最小生成树算法 最短路径算法 邻接矩阵和邻接表的相互转化 求连通分量的算法-Interconversion of various minimum spanning tree algorithm shortest path algorithm adjacency matrix and adjacency list of requirements connected component algorithm
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Fast connected component labeling
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图像的连通区域标记，会将面积较大的两个连通区域标记，在检测肾脏感兴趣区时效果显著。-Connected region labeled image will be larger two connected component labeling, in the detection of renal ROIs effect is remarkable.
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