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首页> 外文会议>2007 IEEE International Conference on Computer-Aided Degign and Computer Graphics >Objects Similarity Measurement Based on Skeleton Tree Descriptor Matching
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Objects Similarity Measurement Based on Skeleton Tree Descriptor Matching

机译：基于骨架树描述符匹配的目标相似度测量

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摘要

In this paper,we proposed a framework to address the problem of binary object (2D or 3D) recognition. In our method,a binary object is represented as a Skeleton Tree (ST),transformed from its skeleton (or centerline). Both topological and geometrical features are embedded in the ST and this allows comparisons between different objects by tree matching algorithms. Tree descriptor is used to represent the topological features of the ST,and the maximal isomorphic subtrees (MIST) are obtained by searching for the longest matching substrings in the tree descriptors. A novel method of ST matching based on tree descriptor is also presented The problems with cyclic skeleton and noise on the skeleton are discussed too.Experiments on a variety of objects get satisfying results,which show the potential of our method in the presence of rotation,scaling,translation and reflection. The time complexity of the algorithm is o(n3),where n is the number of the skeleton branches in ST.
机译：在本文中，我们提出了一个框架来解决二进制对象（2D或3D）识别的问题。在我们的方法中，二进制对象表示为从其骨架（或中心线）转换而来的骨架树（ST）。拓扑和几何特征都嵌入在ST中，这允许通过树匹配算法在不同对象之间进行比较。树描述符用于表示ST的拓扑特征，并通过在树描述符中搜索最长的匹配子串来获得最大同构子树（MIST）。还提出了一种新的基于树描述符的ST匹配方法。还讨论了循环骨架和骨架噪声的问题。对各种物体的实验均获得满意的结果，表明了该方法在存在旋转的情况下的潜力，缩放，平移和反射。该算法的时间复杂度为o（n3），其中n是ST中骨架分支的数量。

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