Bayesian spherical wavelet shrinkage: applications to shape analysis

Xavier Le Faucheur; Brani Vidakovic; Allen Tannenbaum

doi:10.1117/12.734796

2 October 2007 Bayesian spherical wavelet shrinkage: applications to shape analysis

Xavier Le Faucheur, Brani Vidakovic, Allen Tannenbaum

Proceedings Volume 6763, Wavelet Applications in Industrial Processing V; 67630G (2007) https://doi.org/10.1117/12.734796
Event: Optics East, 2007, Boston, MA, United States

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.

Citation Download Citation

Xavier Le Faucheur, Brani Vidakovic, and Allen Tannenbaum "Bayesian spherical wavelet shrinkage: applications to shape analysis", Proc. SPIE 6763, Wavelet Applications in Industrial Processing V, 67630G (2 October 2007); https://doi.org/10.1117/12.734796

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