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Building pattern extraction is one of the crucial components in map generalization. Previous studies mainly focus on the homogeneity between individual buildings in the collinear patterns, while pay less attentions to the complex collinear patterns, in which buildings are locally heterogeneous. Therefore, we put forward a novel combined collinear pattern to extend the existing pattern typology. Then, we present an axis-matching pattern recognition approach which transforms pattern extraction into an axis-matching problem, taking the advantages of computational geometry and visual perception theories. First, we use the axes of the smallest bounding rectangle to represent buildings. Second, different candidate sets of matched axis pairs are built considering the project distance, length and orientation similarities. Third, the strategy for recognizing combined collinear patterns is designed according to similarity criteria. The proposed approach was tested using real topographic data. The experiment results show that the proposed approach is reasonable and efficient, and the correctness and completeness of extracted combined collinear patterns have been guaranteed.

An axis-matching approach to combined collinear pattern recognition for urban building groups