MODELING NON-GAUSSIAN NOISE FOR ROBUST IMAGE ANALYSIS

Sio-Song Ieng, Jean-Philippe Tarel, Pierre Charbonnier

2007

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.

Paper Citation

in Harvard Style

Ieng S., Tarel J. and Charbonnier P. (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

in Bibtex Style

@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},
}

in EndNote Style

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