GRAPHIC SYMBOLS RECOGNITION USING FLEXIBLE MATCHING OF ATTRIBUTED RELATIONAL GRAPHS
GRAPHIC SYMBOLS RECOGNITION USING FLEXIBLE MATCHING OF ATTRIBUTED RELATIONAL GRAPHS
复制标题
使用属性关系图的灵活匹配进行图形符号识别
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
Cardot
中科院分区：
文献类型：
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作者：
Rashid Jalal;Jean;Hubert Ramel;Cardot
图表示和图匹配已经成功地应用于计算机视觉和模式识别中的大量问题。在图匹配方面，经典的图同构算法在图像噪声或矢量失真的情况下显得毫无用处。本文提出了一种新的相似性度量，通过属性图的不精确匹配来识别符号。在所提出的方法中，符号编码的属性图，其节点表示结构图元，如四边形和边缘表示这些图元之间的相互关系。为了保持旋转和缩放不变，将图元的相关信息关联为节点和边缘上的属性。考虑到两个图之间的映射，制定了一个相似性函数，使用属性的数值来计算相似性得分。这种新的相似性度量具有许多理想的属性，如鉴别力，仿射变换不变，噪声或矢量失真的鲁棒性。
Graph representation and graph matching have been successfully applied to a large number of problems in computer vision and pattern recognition. Concerning graph matching, the classical algorithms of graph isomorphism seems useless when the image is degraded with noise or vectorial distortion. This paper introduce a novel similarity measure to recognize symbols by performing inexact matching of attributed graphs. In the proposed approach, symbols are encoded by attributed graphs, whose nodes represent structural primitives like quadrilaterals and whose edges represent mutual relationships between these primitives. To be invariant of rotation and scaling, relative information about primitives are associated as attributes on the nodes and edges. Considering a mapping between two graphs, a similarity function is formulated, that use the numerical values of the attributes to calculate a similarity score. This new similarity measure has many desirable properties such as discrimination power, invariant to affine transformations, and robustness to noise or vectorial distortions.