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Probabilistic relaxation for matching problems in computer vision

机译:概率放松,用于匹配计算机视觉中的问题

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The authors present the theory of probabilistic relaxation for matching symbolic structures, derive as limiting cases the various heuristic formulas used by researchers in matching problems, and state the conditions under which they apply. They successfully apply the theory to the problem of matching and recognizing aerial road network images based on road network models and to the problem of edge matching in a stereo pair. For this purpose, each line network is represented by an attributed relational graph where each node is a straight line segment characterized by certain attributes and related with every other node via a set of binary relations.
机译:作者呈现了匹配符号结构的概率松弛理论,因为研究人员在匹配问题中使用的各种启发式公式的限制性案例,以及陈述它们所申请的条件。他们成功地将理论应用于基于道路网模型的匹配和识别空中路网图像的问题,并对立体对中的边缘匹配问题。为此目的,每个线路网络由属性关系图表示,其中每个节点是由某些属性的特征的直线段,并且通过一组二进制关系与每个其他节点相关。

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