In this paper, we propose a novel shape representation we call directional histogram model (DHM). It captures the shape variation of an object and is invariant to scaling and rigid transforms. The DHM is computed by first extracting a directional distribution of thickness histogram signatures, which are translation invariant. We show how the extraction of the thickness histogram distribution can be accelerated using conventional graphics hardware. Orientation invariance is achieved by computing the spherical harmonic transform of this distribution. Extensive experiments show that the DHM is capable of high discrimination power and is robust to noise.
Directional histogram model for three-dimensional shape similarity
01.01.2003
530154 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Directional Histogram Model for Three-Dimensional Shape Similarity
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