期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2006
卷号：6
期号：9A
页码：119-123
出版社：International Journal of Computer Science and Network Security
摘要：This paper presents a Bayesian-based method for classifying 3D objects into a set of pre-determined object classes. The basic idea is to determine a set of most similar three-dimensional objects. The three-dimensional models have to consider spatial properties such as shape. We use curvature as an intuitive and powerful similarity index for three-dimensional objects which consists of a histogram of the principal curvatures of each face of the mesh. An experimental evaluation demonstrates the satisfactory performance of our approach on a fifty three-dimensional models database.
