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- W2765228520 abstract "To solve the problem of automatic recognition in the presence of significant geometric and topological variations of 3D shape components, a semantic relation driven SVM-based function recognition method is proposed. Firstly, the shape segmentation scheme based on approximate convexity decomposition is proposed to decompose the shape into shape components with different semantics. Secondly, a functional semantic similarity method based on component context relations is presented to qualitatively measure semantic relations between the obtained shape components. Finally, the SVM classifier with functional semantic similarity as kernel function is constructed to achieve the task of shape recognition. Experimental results show that the proposed method could improve the accuracy of function recognition of 3D shapes, especially for shapes with large-scale deformation." @default.
- W2765228520 created "2017-11-10" @default.
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- W2765228520 date "2017-10-27" @default.
- W2765228520 modified "2023-09-27" @default.
- W2765228520 title "Semantic Relation Driven SVM-Based Function Recognition for 3D Shape Components" @default.
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- W2765228520 doi "https://doi.org/10.1007/978-981-10-6445-6_8" @default.
- W2765228520 hasPublicationYear "2017" @default.
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