Motion detection in meteorological images sequences: two methods and their comparison.

Dominique Béréziat 1 Isabelle Herlin 1 Laurent Younes 2
1 AIR - Image Processing and Dynamic and Satellite Data
Inria Paris-Rocquencourt
2 CMLA - Centre de Mathématiques et de Leurs Applications
Résumé : This study presents and compares two models for estimating motion in meteorological images sequences. The first method makes use of the grey level pixel conservation hypothesis. It produces a dense vector field through a variational formulation, and authorizes discontinuities in the resulting field. A second method use a model taking affine motion as ground hypothesis. Motion parameters are then estimated with an incremental least-square procedure. One of its principal advantages results in a modeling of the variation of the grey level values. The two methods are complementary: the second computes a global estimation of the motion, which is locally enhanced by the first.
Type de document :
Communication dans un congrès
Image Processing, Signal Processing, and Synthetic Aperture Radar for Remote Sensing, Sep 1997, London, United Kingdom. 3217, 1997, 〈10.1117/12.295618〉
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https://hal.inria.fr/inria-00532708
Contributeur : Brigitte Briot <>
Soumis le : jeudi 4 novembre 2010 - 13:39:24
Dernière modification le : vendredi 25 mai 2018 - 12:02:05

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Dominique Béréziat, Isabelle Herlin, Laurent Younes. Motion detection in meteorological images sequences: two methods and their comparison.. Image Processing, Signal Processing, and Synthetic Aperture Radar for Remote Sensing, Sep 1997, London, United Kingdom. 3217, 1997, 〈10.1117/12.295618〉. 〈inria-00532708〉

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