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谢艳文, 张慧. 基于2-邻域局部结构的矢量图符号模糊识别方法[J]. 计算机辅助设计与图形学学报, 2014, 26(10): 1613-1623.
引用本文: 谢艳文, 张慧. 基于2-邻域局部结构的矢量图符号模糊识别方法[J]. 计算机辅助设计与图形学学报, 2014, 26(10): 1613-1623.
Xie Yanwen, Zhang Hui. Research on Symbol Fuzzy Recognition in Vector Drawings Based on 2-Neighborhood Local Structures[J]. Journal of Computer-Aided Design & Computer Graphics, 2014, 26(10): 1613-1623.
Citation: Xie Yanwen, Zhang Hui. Research on Symbol Fuzzy Recognition in Vector Drawings Based on 2-Neighborhood Local Structures[J]. Journal of Computer-Aided Design & Computer Graphics, 2014, 26(10): 1613-1623.

基于2-邻域局部结构的矢量图符号模糊识别方法

Research on Symbol Fuzzy Recognition in Vector Drawings Based on 2-Neighborhood Local Structures

  • 摘要: 针对矢量图纸中经常出现的符号模糊性进行分类定义,提出一种基于2-邻域局部结构的符号识别方法.该方法利用局部结构的思想,首先将原型符号和目标图纸统一表示成2-邻域局部结构的集合,并定义2-邻域局部结构的相似距离;进而根据距离度量筛选出相似的局部结构;最后对目标图纸中的相似局部结构按照空间距离和缩放因子进行聚类,获得与原型符号相似的符号实例.实验结果表明,文中方法不仅可以解决包含重复结构、可伸缩等模糊性的符号识别问题,还能够保持较高的识别准确率.

     

    Abstract: This paper gives a classification of symbol fuzziness and proposes an approach based on 2-neighborhood local structures to recognize fuzzy symbols in vector drawings.By using the idea of local structures, the approach first represents the symbol to be searched and the target vector drawing as two sets of 2-neighborhood local structures, and defines the similarity distance between two local structures, then select out the similar structures according to the similarity distance.Finally, the approach clusters the similar structures based on their space distance and scale factor to find fuzzy symbols in the vector drawing.The results show that the approach can deal with fuzzy symbols with duplicate structures or stretching and gives high recognition accuracy with different kinds of fuzziness.

     

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