Shape decomposition and skeletonization share many common properties and applications. However, they are generally considered as independent methods. In many applications, the detailed features of a model are not crucial and in fact considering them only may serve to obscure the important structural features and adds to the processing cost. In such cases, an approximate representation of the model, such as approximate convex decomposition (ACD), that captures the key structural features would be preferable. One important example is skeleton extraction. The skeleton is a low dimensional object which essentially represents the "shape" of the higher-dimensional target object. The process of generating such a skeleton is called skeleton extraction. ACD partitions a model into nearly convex components, has been shown to reveal important structural information and is used for shape decomposition in this paper. A skeleton of the model is then extracted from the convex hulls of these nearly convex components. The process of simultaneous shape decomposition and skeletonization iterates until the quality of the skeleton becomes satisfactory.
Approximate Convex Decomposition and Its Applications, Jyh-Ming Lien, Ph.D. Thesis, Department of Computer Science and Engineering, Texas A&M University, Dec 2006.
Ph.D. Thesis(pdf, abstract)
Simultaneous Shape Decomposition and Skeletonization, Jyh-Ming Lien, John Keyser, Nancy M. Amato, In Proc. ACM Solid and Physical Modeling Symp. (SPM), pp. 219-228, Cardiff, Wales, UK, Jun 2006.
Supported by NSF
Project Alumni:Jyh-Ming Lien
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