Segmentation of animation character model by combining semantic ontology and Poisson equation
PAN Xiang1, TONG Wei huai1, ZHANG San yuan 2, ZHENG He rong1
1.College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China;2.College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China
A segmentation algorithm was proposed based on semantic ontology to solve the problem that animation character model is inconsistent because of the changing of poses. The segmentation ontology was constructed according to the map between semantic label and geometrical characteristic. In the process of segmentation, the semantic label of segmented parts was recognized using support vector machine (SVM) and geometrical characteristic. In this way, the hierarchical segmentation of dynamic models could be consistent with the help of semantic label. In addition, the length of boundary was almost equal under different poses, therefore, segmenting boundary was refined by Poisson equation. The refined method made the animation character models smooth and consistent. In experiment, the segmenting result of the tested dynamic models show that the proposed algorithm is very stable. Compared with the existing methods, segmentation ontology can select optimizing parameters for different 3D shapes. As a result, the proposed method can achieve boundary of segmenting quality over the exiting methods.
PAN Xiang, TONG Wei huai, ZHANG San yuan,ZHENG He rong. Segmentation of animation character model by combining semantic ontology and Poisson equation. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2015, 49(9): 1634-1641.
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