Authors:
Patrick Héas
and
Etienne Mémin
Affiliation:
INRIA - IRISA, France
Keyword(s):
3D Motion estimation, Variational methods, Optical-flow, Atmospheric dynamics, Physical-based model.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Human-Computer Interaction
;
Image-Based Modeling
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Software Engineering
Abstract:
In this paper, we address the problem of estimating three-dimensional motions of a stratified atmosphere from satellite image sequences. The complexity of three-dimensional atmospheric fluid flows associated to incomplete observation of atmospheric layers due to the sparsity of cloud systems makes very difficult the estimation of dense atmospheric motion field from satellite images sequences. The recovery of the vertical component of fluid motion from a monocular sequence of image observations is a very challenging problem for which no solution exists in the literature. Based on a physically sound vertical decomposition of the atmosphere into layers of different altitudes, we propose here a dense motion estimator dedicated to the extraction of three-dimensional wind fields characterizing the dynamics of a layered atmosphere. Wind estimation is performed over the complete three-dimensional space using a multi-layer model describing a stack of dynamic horizontal layers of evolving thic
kness, interacting at their boundaries via vertical winds. The efficiency of our approach is demonstrated on synthetic and real sequences.
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