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Authors: Sio-Song Ieng 1 ; Jean-Philippe Tarel 2 and Pierre Charbonnier 3

Affiliations: 1 ERA 17 LCPC, Laboratoire des Ponts et Chaussées, France ; 2 ESE, Laboratoire Central des Ponts et Chaussées, France ; 3 ERA 27 LCPC, Laboratoire des Ponts et Chaussées, France

ISBN: 978-972-8865-73-3

Keyword(s): Image Analysis, Statistical Approach, Noise Modeling, Robust Fitting, Image Grouping and Segmentation, Image Enhancement.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Enhancement and Restoration ; Image and Video Analysis ; Image Formation and Preprocessing ; Statistical Approach

Abstract: Accurate noise models are important to perform reliable robust image analysis. Indeed, many vision problems can be seen as parameter estimation problems. In this paper, two noise models are presented and we show that these models are convenient to approximate observation noise in different contexts related to image analysis. In spite of the numerous results on M-estimators, their robustness is not always clearly addressed in the image analysis field. Based on Mizera and Mu¨ ller’s recent fundamental work, we study the robustness of M-estimators for the two presented noise models, in the fixed design setting. To illustrate the interest of these noise models, we present two image vision applications that can be solved within this framework: curves fitting and edge-preserving image smoothing.

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Paper citation in several formats:
Ieng S.; Tarel J.; Charbonnier P. and (2007). MODELING NON-GAUSSIAN NOISE FOR ROBUST IMAGE ANALYSIS.In Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, ISBN 978-972-8865-73-3, pages 183-190. DOI: 10.5220/0002040901830190

@conference{visapp07,
author={Sio{-}Song Ieng and Jean{-}Philippe Tarel and Pierre Charbonnier},
title={MODELING NON-GAUSSIAN NOISE FOR ROBUST IMAGE ANALYSIS},
booktitle={Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,},
year={2007},
pages={183-190},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002040901830190},
isbn={978-972-8865-73-3},
}

TY - CONF

JO - Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,
TI - MODELING NON-GAUSSIAN NOISE FOR ROBUST IMAGE ANALYSIS
SN - 978-972-8865-73-3
AU - Ieng, S.
AU - Tarel, J.
AU - Charbonnier, P.
PY - 2007
SP - 183
EP - 190
DO - 10.5220/0002040901830190

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