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Authors: Teodor Cioaca 1 ; Bogdan Dumitrescu 1 and Mihai Sorin Stupariu 2

Affiliations: 1 University Politehnica Bucharest, Romania ; 2 University of Bucharest, Romania

ISBN: 978-989-758-224-0

Keyword(s): Graph Wavelets, Multi-variate Mesh Signals, Riemannian Mean Shift.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Fundamental Methods and Algorithms ; Geometric Computing ; Geometry and Modeling ; Modeling and Algorithms ; Surface Modeling

Abstract: Designing filters over irregular non-Euclidean domains requires algorithms that take into account the intrinsic curvature of these domains. We propose a new filtering method based on Riemannian weighted averages. The resulting filters are non-Euclidean adaptations of the mean shift and blurring mean shift algorithms. We also introduce a hybrid, efficient computing strategy by combining these iterative filtering methods with wavelet multi-resolution editing. The applications of our filters include multi-variate mesh data smoothing, denoising, attribute enhancement and curvature filtering.

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Paper citation in several formats:
Cioaca, T.; Dumitrescu, B. and Stupariu, M. (2017). Riemannian Filters for Multi-variate Mesh Signals.In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2017) ISBN 978-989-758-224-0, pages 228-235. DOI: 10.5220/0006128602280235

@conference{grapp17,
author={Teodor Cioaca. and Bogdan Dumitrescu. and Mihai Sorin Stupariu.},
title={Riemannian Filters for Multi-variate Mesh Signals},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2017)},
year={2017},
pages={228-235},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006128602280235},
isbn={978-989-758-224-0},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2017)
TI - Riemannian Filters for Multi-variate Mesh Signals
SN - 978-989-758-224-0
AU - Cioaca, T.
AU - Dumitrescu, B.
AU - Stupariu, M.
PY - 2017
SP - 228
EP - 235
DO - 10.5220/0006128602280235

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