Density-based 3D shape descriptors. Akgul, C. B., Sankur, B., Yemez, Y., & Schmitt, F. EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, 2007. doi abstract bibtex 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. Copyright (c) 2007 Ceyhun Burak Akgul et al.
@article{ ISI:000247955900001,
Author = {Akgul, Ceyhun Burak and Sankur, Bulent and Yemez, Yucel and Schmitt,
Francis},
Title = {{Density-based 3D shape descriptors}},
Journal = {{EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING}},
Year = {{2007}},
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. Copyright (c) 2007 Ceyhun
Burak Akgul et al.}},
DOI = {{10.1155/2007/32503}},
Article-Number = {{32503}},
ISSN = {{1687-6180}},
Unique-ID = {{ISI:000247955900001}},
}
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