Dense estimation of fluid flows
Corpetti, T.
Memin, E.
Perez, P.
IRISA, Rennes I Univ.;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Mar 2002
Volume: 24,
Issue: 3
On page(s): 365-380
ISSN: 0162-8828
References Cited: 48
CODEN: ITPIDJ
INSPEC Accession Number: 7223260
Digital Object Identifier: 10.1109/34.990137
Current Version Published: 2002-08-07
Abstract
In this paper, we address the problem of estimating and analyzing
the motion of fluids in image sequences. Due to the great deal of
spatial and temporal distortions that intensity patterns exhibit in
images of fluids, the standard techniques from computer vision,
originally designed for quasi-rigid motions with stable salient
features, are not well adapted in this context. We thus investigate a
dedicated minimization-based motion estimator. The cost function to be
minimized includes a novel data term relying on an integrated version of
the continuity equation of fluid mechanics, which is compatible with
large displacements. This term is associated with an original
second-order div-curl regularization which prevents the washing out of
the salient vorticity and divergence structures. The performance of the
resulting fluid flow estimator is demonstrated on meteorological
satellite images. In addition, we show how the sequences of dense motion
fields we estimate can be reliably used to reconstruct trajectories and
to extract the regions of high vorticity and divergence
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