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Authors: Dominique Béréziat 1 ; Isabelle Herlin 2 and Yann Lepoittevin 2

Affiliations: 1 Sorbonne Universités and UPMC Univ. Paris 06, France ; 2 Inria, France

ISBN: 978-989-758-227-1

Keyword(s): Domain Decomposition, Ensemble Kalman Filter, Fluid Flows, Localization, Motion Estimation.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Motion, Tracking and Stereo Vision ; Optical Flow and Motion Analyses

Abstract: One major limitation of the motion estimation methods that are available in the literature concerns the availability of the uncertainty on the result. This is however assessed by a number of filtering methods, such as the ensemble Kalman filter (EnKF). The paper consequently discusses the use of a description of the displayed structures in an ensemble Kalman filter, which is applied for estimating motion on image acquisitions. An example of such structure is a cloud on meteorological satellite acquisitions. Compared to the Kalman filter, EnKF does not require propagating in time the error covariance matrix associated to the estimation, resulting in reduced computational requirements. However, EnKF is also known for exhibiting a shrinking effect when taking into account the observations on the studied system at the analysis step. Methods are available in the literature for correcting this shrinking effect, but they do not involve the spatial content of images and more specifically t he structures that are displayed on the images. Two solutions are described and compared in the paper, which are first a dedicated localization function and second an adaptive domain decomposition. Both methods proved being well suited for fluid flows images, but only the domain decomposition is suitable for an operational setting. In the paper, the two methods are applied on synthetic data and on satellite images of the atmosphere, and the results are displayed and evaluated. (More)

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Paper citation in several formats:
Béréziat, D.; Herlin, I. and Lepoittevin, Y. (2017). Ensemble Kalman Filter based on the Image Structures.In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-227-1, pages 140-150. DOI: 10.5220/0006096201400150

@conference{visapp17,
author={Dominique Béréziat. and Isabelle Herlin. and Yann Lepoittevin.},
title={Ensemble Kalman Filter based on the Image Structures},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={140-150},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006096201400150},
isbn={978-989-758-227-1},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)
TI - Ensemble Kalman Filter based on the Image Structures
SN - 978-989-758-227-1
AU - Béréziat, D.
AU - Herlin, I.
AU - Lepoittevin, Y.
PY - 2017
SP - 140
EP - 150
DO - 10.5220/0006096201400150

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