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Edge-Preserving Integration of a Normal Field: Weighted Least Squares and L1 Approaches

Quéau, Yvain and Durou, Jean-Denis Edge-Preserving Integration of a Normal Field: Weighted Least Squares and L1 Approaches. (2015) In: 5th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2015), 31 May 2015 - 4 June 2015 (Lège Cap Ferret, France).

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Official URL: http://dx.doi.org/10.1007/978-3-319-18461-6_46

Abstract

We introduce several new functionals, inspired from variational image denoising models, for recovering a piecewise-smooth surface from a dense estimation of its normal field. In the weighted least-squares approach, the non-differentiable elements of the surface are a priori detected so as to weight the least-squares model. To avoid this detection step, we introduce reweighted least-squares for minimising an isotropic TV-like functional, and split-Bregman iterations for L1 minimisation.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to Springer editor. This papers appears in Volume 9087 Lecture Notes in Computer Science ISSN : 0302-9743. ISBN: 978-3-319-18460-9. The original PDF is available at: http://link.springer.com/chapter/10.1007%2F978-3-319-18461-6_46
HAL Id:hal-01360871
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UPS (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
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Deposited By: IRIT IRIT
Deposited On:07 Jul 2016 08:31

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