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Proceedings Paper

Bayesian spherical wavelet shrinkage: applications to shape analysis
Author(s): Xavier Le Faucheur; Brani Vidakovic; Allen Tannenbaum
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Paper Abstract

Multiscale analysis has become indispensable in image processing and computer vision. Our work is motivated by the need to efficiently represent 3D shapes that exhibit a spherical topology. This note presents a wavelet based model for shape denoising and data compression. The 3D shape signal is first encoded using biorthogonal spherical wavelet functions defined on a 3D triangulated mesh. We propose a Bayesian shrinkage model for this type of second generation wavelets in order to eliminate wavelet coefficients that likely correspond to noise. This way, we are able to reduce dimension without losing significant information by estimating a noiseless version of our shape.

Paper Details

Date Published: 2 October 2007
PDF: 11 pages
Proc. SPIE 6763, Wavelet Applications in Industrial Processing V, 67630G (2 October 2007); doi: 10.1117/12.734796
Show Author Affiliations
Xavier Le Faucheur, Georgia Institute of Technology (United States)
Brani Vidakovic, Georgia Institute of Technology (United States)
Allen Tannenbaum, Georgia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 6763:
Wavelet Applications in Industrial Processing V
Frédéric Truchetet; Olivier Laligant, Editor(s)

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