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Title
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A Bayesian Decision for 3D Object Retrieval and Classification
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Author
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Abdelalim Sadiq, Rachid Oulad Haj Thami
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Citation |
Vol. 6 No. 9 pp. 119-123
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Abstract
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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.
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Keywords
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3D Object, Bayesian classification, Curvature Index, 3D/3D indexing, 2D/3D Indexing
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URL
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http://paper.ijcsns.org/07_book/200609/200609A18.pdf
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