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Title

A Bayesian Decision for 3D Object Retrieval and Classification

Author

Abdelalim Sadiq, Rachid Oulad Haj Thami

Citation

Vol. 6  No. 9  pp. 119-123

Abstract

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.

Keywords

3D Object, Bayesian classification, Curvature Index, 3D/3D indexing, 2D/3D Indexing

URL

http://paper.ijcsns.org/07_book/200609/200609A18.pdf