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Triangulation based topology approach for 2D point sets registration

机译:二维点集配准的基于三角剖分的拓扑方法

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摘要

Registration is designed to overcome global spatial correspondence ambiguities of different spatial databases and is required as a first stage in integration. It aims at ensuring an accurate and qualitative match while avoiding erroneous or coinciding data-features in the final product. The algorithm presented in this research paper describes an automatic constraint-free robust homological feature based registration of two 2D point sets. The points represent features of interest that exist in spatial databases, e.g. maps and topographic datasets. The algorithm entails an iterative aggregative voting process that consists of a set of qualitative statistical quantifications, based on the correspondences of triangulation structure, aimed at evaluating the geometric similarity of the data. The algorithm is structured to overcome data ambiguities, including data outliers and noise. The automatic aggregative voting algorithm replaces the need for a-priori spatial knowledge or the use of manual or semi-automatic registration that is prone to error. A comparison of the proposed automatic registration with alternative commonly used processes is presented, showing better and robust results. As such, the presented algorithm proves as an important stage towards the qualitative integration and enhancement of spatial databases.
机译:旨在克服不同空间数据库的全局空间对应歧义的注册,这是集成的第一步。它旨在确保准确和定性的匹配,同时避免最终产品中的错误或一致的数据功能。本研究论文中提出的算法描述了一种基于自动无约束的鲁棒同源特征的两个2D点集的配准。这些点表示空间数据库中存在的感兴趣的特征,例如地图和地形数据集。该算法需要一个迭代的集合投票过程,该过程由一组定性的统计量化组成,该定量量化基于三角测量结构的对应关系,旨在评估数据的几何相似性。该算法旨在克服数据歧义,包括数据异常值和噪声。自动集合投票算法取代了对先验空间知识或易于出错的手动或半自动注册的需求。将建议的自动注册与替代的常用过程进行了比较,显示了更好,更可靠的结果。这样,所提出的算法被证明是对空间数据库进行定性集成和增强的重要阶段。

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