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Efficient Subgraph Isomorphism with“A Priori” Knowledge: Application to 3D Reconstruction of Buildings for Cartography

机译:高效的子图同样,“先验”知识:应用于3D重建建筑物的制图

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In this paper, a procedure which computes error-correcting subgraph isomorphisms is proposed in order to be able to take into account some external information. When matching a model graph and a data graph, if the correspondance between vertices of the model graph and some vertices of the data graph are known “a priori”, the procedure is able to integrate this knowledge in an efficient way. The efficiency of the method is obtained in the first step of the procedure, namely, by the recursive decomposition of the model graph into subgraphs. During this step, these external information are propagated as far as possible thanks to a new procedure which makes the graphs able to share them. Since the data structure is now able to fully integrate the external information, the matching step itself becomes more efficient. The theoretical aspects of this methodology are presented, as well as practical experiments on real images. The procedure is tested in the field of 3-D building reconstruction for cartographic issues, where it allows to match model graphs partially, and then perform full matches.
机译:在本文中,提出了一种计算纠错子图同构的过程,以便能够考虑一些外部信息。当匹配模型图和数据图时,如果模型图的顶点与数据图的某些顶点之间的对应是已知的“先验”,则该过程能够以有效的方式集成这些知识。在步骤的第一步中获得该方法的效率,即,通过模型图的递归分解为子图。在此步骤中,由于新的过程,这些外部信息尽可能远离传播,这使得可以共享图形。由于数据结构现在能够完全集成外部信息,因此匹配的步骤本身变得更有效。提出了该方法的理论方面,以及实际图像的实际实验。该过程在3-D构建重建领域进行了用于制图问题,其中它允许部分地匹配模型图,然后执行完整匹配。

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