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Density-Based 3D Shape Descriptors

  • 1, 2Email author,
  • 1,
  • 3 and
  • 2
EURASIP Journal on Advances in Signal Processing20062007:032503

https://doi.org/10.1155/2007/32503

  • Received: 1 February 2006
  • Accepted: 10 September 2006
  • Published:

Abstract

We propose a novel probabilistic framework for the extraction of density-based 3D shape descriptors using kernel density estimation. Our descriptors are derived from the probability density functions (pdf) of local surface features characterizing the 3D object geometry. Assuming that the shape of the 3D object is represented as a mesh consisting of triangles with arbitrary size and shape, we provide efficient means to approximate the moments of geometric features on a triangle basis. Our framework produces a number of 3D shape descriptors that prove to be quite discriminative in retrieval applications. We test our descriptors and compare them with several other histogram-based methods on two 3D model databases, Princeton Shape Benchmark and Sculpteur, which are fundamentally different in semantic content and mesh quality. Experimental results show that our methodology not only improves the performance of existing descriptors, but also provides a rigorous framework to advance and to test new ones.

Keywords

  • Probability Density Function
  • Quantum Information
  • Density Estimation
  • Local Surface
  • Kernel Density

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Authors’ Affiliations

(1)
Electrical and Electronics Engineering Department, Boğaziçi University, Bebek, Istanbul, 34342, Turkey
(2)
GET-Telecom Paris, CNRS UMR 5141, Paris, Cedex 13 75634, France
(3)
Computer Engineering Department, Koç University, Sariyer, Istanbul, 34450, Turkey

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Copyright

© Ceyhun Burak Akgül et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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