统计两个图的连通分量的个数。
Statistics of two graphs the number of connected components.
算法的关键是判断一个结点是否是强连通分量的根。
The crux of the algorithm comes in determining whether a node is the root of a strongly connected component.
这个根结点是在深搜时碰到当前强连通分量的第一个结点。
The root node is simply the first node of the strongly connected component which is encountered during the depth-first traversal.
强连通分量形成了搜索树，他们的根就是强连通分量的根。
The strongly connected components form the subtrees of the search tree, the roots of which are the roots of the strongly connected components.
算法的输入是一个有向图，产生一个图的强连通分量顶点划分。
The algorithm takes a directed graph as input, and produces a partition of the graph's vertices into the graph's strongly connected components.
当从一个搜索树返回时，判断该点是否是一个强连通分量的根。
When the search returns from a subtree, the nodes are taken from the stack and it is determined whether each node is the root of a strongly connected component.
该算法将自适应取阈方法和连通分量分析方法集成于一次图像扫描处理中实现。
The hybrid algorithm integrates adaptive thresholding and connected-components analysis into one pass of image processing.
现在有一些文献对有向图的强连通分量做了一些讨论，一般采用了递归的方法。
Many documents and papers nowadays have discussed the strong connected component of the directed graph, and the method they adopted is usually Recursion.
结合连通分量提取及垂直投影分割算法，可以有效地获取车牌的最优分割结果及其置信度。
With the combination of connected-component-based and vertical-projection-based algorithms, the optimized segmentation result and its confidence level can be obtained.
图像的几何性质，比如区域周长和连通分量，在图像分割和模式识别领域得到了广泛的应用。
Geometric properties, such as perimeter and connected component, have been widely used in image segmentation and pattern recognition.
为了更有效地进行文本检测与分割，提出了一种基于连通分量特征的自然场景中文本检测分割算法。
To detect and segment text effectively, this paper proposes an approach for detecting and segmenting text from scene images by using Connected-Components' features.
根据最小生成树(MST)算法获得图像序列的连通分量，得到图像对之间的变换矩阵并将图像映射到拼接平面。
The minimum spanning tree (MST) was used to obtain the best connected-component of the image set to recover the transformation between images and project the images into the Mosaic frame.
以自适应门限值提取前景区域，通过扩展的连通分量提取算法实现了目标的快速搜索，最后几何特征对目标加以识别。
Foreground area is extracted by adaptive threshold method, the object is searched by extending the connected components extraction method, the object is recognized based on the geometrical character.
由于文本连通分量和非文本连通分量在特征上存在差异，大多数非文本会被级联分类器丢弃，而SVM则能在此结果上做进一步的验证，因此最终输出只有文本的二值图像。
Most of non-text CCs are filtered out by cascade classifier and the remaining CCs are further verified by SVM. The final outputs are binary images containing texts only.
由于文本连通分量和非文本连通分量在特征上存在差异，大多数非文本会被级联分类器丢弃，而SVM则能在此结果上做进一步的验证，因此最终输出只有文本的二值图像。
Most of non-text CCs are filtered out by cascade classifier and the remaining CCs are further verified by SVM. The final outputs are binary images containing texts only.
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