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基于图形的形状表示和识别

Graph Based Shapes Representation and Recognition
课程网址: http://videolectures.net/gbr07_qureshi_gbsr/  
主讲教师: Rashid Jalal Qureshi
开课单位: 弗朗索瓦拉伯莱大学
开课时间: 2007-07-12
课程语种: 英语
中文简介:
在本文中,我们建议通过图表来表示形状。基于从二值图像中提取的图形基元,生成属性关系图。因此,图的节点表示形状基元,如矢量和四边形,而弧描述相互基元关系。为了对诸如旋转和缩放的变换不变,从基元提取的相对几何特征与节点和边缘相关联作为属性。关于图匹配,由于图子图同构的NP完备性,对不精确图匹配的不同策略给予了相当的关注。我们还提出了一个新的评分函数,用于计算两个图之间的相似性得分,使用与图的节点和边相关联的数值。贪婪图匹配算法与新评分函数的匹配表明,与传统的图匹配穷举搜索相比,性能有了显着提高。
课程简介: In this paper, we propose to represent shapes by graphs. Based on graphic primitives extracted from the binary images, attributed relational graphs were generated. Thus, the nodes of the graph represent shape primitives like vectors and quadrilaterals while arcs describing the mutual primitives relations. To be invariant to transformations such as rotation and scaling, relative geometric features extracted from primitives are associated to nodes and edges as attributes. Concerning graph matching, due to the fact of NP-completeness of graph-subgraph isomorphism, a considerable attention is given to different strategies of inexact graph matching. We also present a new scoring function to compute a similarity score between two graphs, using the numerical values associated to the nodes and edges of the graphs. The adaptation of a greedy graph matching algorithm with the new scoring function demonstrates significant performance improvements over traditional exhaustive searches of graph matching.
关 键 词: 二值图像; 图形基元; 相似性
课程来源: 视频讲座网
最后编审: 2019-05-26:cwx
阅读次数: 36