Estimation of Dense Image Flow Fields in Fluids
The estimation of flow fields from time sequences of satellite imagery has a number of important applications. For visualisation of cloud or sea ice movements in sequences of crude temporal sampling a satisfactory non-blurred temporal interpolation can be performed only when the flow field or an est...
Published in: | IEEE Transactions on Geoscience and Remote Sensing |
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Language: | English |
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1998
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ftdtupubl:oai:pure.atira.dk:publications/c7b23ec7-7c52-4904-a2d6-d7867fac848b 2023-10-01T03:59:23+02:00 Estimation of Dense Image Flow Fields in Fluids Larsen, Rasmus Conradsen, Knut Ersbøll, Bjarne Kjær 1998 application/pdf https://orbit.dtu.dk/en/publications/c7b23ec7-7c52-4904-a2d6-d7867fac848b https://doi.org/10.1109/36.655334 https://backend.orbit.dtu.dk/ws/files/4365342/Larsen.pdf eng eng info:eu-repo/semantics/openAccess Larsen , R , Conradsen , K & Ersbøll , B K 1998 , ' Estimation of Dense Image Flow Fields in Fluids ' , Geoscience and Remote Sensing, IEEE Transactions on , vol. 36 , no. 1 , pp. 256-264 . https://doi.org/10.1109/36.655334 article 1998 ftdtupubl https://doi.org/10.1109/36.655334 2023-09-06T22:56:51Z The estimation of flow fields from time sequences of satellite imagery has a number of important applications. For visualisation of cloud or sea ice movements in sequences of crude temporal sampling a satisfactory non-blurred temporal interpolation can be performed only when the flow field or an estimate there-of is known. Estimated flow fields in weather satellite imagery might also be used on an operational basis as inputs to short-term weather prediction. In this article we describe a method for the estimation of dense flow fields. Local measurements of motion are obtained by analysis of the local energy distribution, which is sampled using a set of 3-D spatio-temporal filters. The estimated local energy distribution also allows us to compute a confidence measure of the estimated local normal flow. The algorithm furthermore utilises Markovian random fields in order to integrate the local estimates of normal flows into a dense flow field using measures of spatial smoothness. To obtain smoothness we will constrain first order derivatives of the flow field. The performance of the algorithm is illustrated by the estimation of the flow fields corresponding to a sequence of Meteosat thermal images. The estimated flow fields are used in a temporal interpolation scheme. Article in Journal/Newspaper Sea ice Technical University of Denmark: DTU Orbit IEEE Transactions on Geoscience and Remote Sensing 36 1 256 264 |
institution |
Open Polar |
collection |
Technical University of Denmark: DTU Orbit |
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ftdtupubl |
language |
English |
description |
The estimation of flow fields from time sequences of satellite imagery has a number of important applications. For visualisation of cloud or sea ice movements in sequences of crude temporal sampling a satisfactory non-blurred temporal interpolation can be performed only when the flow field or an estimate there-of is known. Estimated flow fields in weather satellite imagery might also be used on an operational basis as inputs to short-term weather prediction. In this article we describe a method for the estimation of dense flow fields. Local measurements of motion are obtained by analysis of the local energy distribution, which is sampled using a set of 3-D spatio-temporal filters. The estimated local energy distribution also allows us to compute a confidence measure of the estimated local normal flow. The algorithm furthermore utilises Markovian random fields in order to integrate the local estimates of normal flows into a dense flow field using measures of spatial smoothness. To obtain smoothness we will constrain first order derivatives of the flow field. The performance of the algorithm is illustrated by the estimation of the flow fields corresponding to a sequence of Meteosat thermal images. The estimated flow fields are used in a temporal interpolation scheme. |
format |
Article in Journal/Newspaper |
author |
Larsen, Rasmus Conradsen, Knut Ersbøll, Bjarne Kjær |
spellingShingle |
Larsen, Rasmus Conradsen, Knut Ersbøll, Bjarne Kjær Estimation of Dense Image Flow Fields in Fluids |
author_facet |
Larsen, Rasmus Conradsen, Knut Ersbøll, Bjarne Kjær |
author_sort |
Larsen, Rasmus |
title |
Estimation of Dense Image Flow Fields in Fluids |
title_short |
Estimation of Dense Image Flow Fields in Fluids |
title_full |
Estimation of Dense Image Flow Fields in Fluids |
title_fullStr |
Estimation of Dense Image Flow Fields in Fluids |
title_full_unstemmed |
Estimation of Dense Image Flow Fields in Fluids |
title_sort |
estimation of dense image flow fields in fluids |
publishDate |
1998 |
url |
https://orbit.dtu.dk/en/publications/c7b23ec7-7c52-4904-a2d6-d7867fac848b https://doi.org/10.1109/36.655334 https://backend.orbit.dtu.dk/ws/files/4365342/Larsen.pdf |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
Larsen , R , Conradsen , K & Ersbøll , B K 1998 , ' Estimation of Dense Image Flow Fields in Fluids ' , Geoscience and Remote Sensing, IEEE Transactions on , vol. 36 , no. 1 , pp. 256-264 . https://doi.org/10.1109/36.655334 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1109/36.655334 |
container_title |
IEEE Transactions on Geoscience and Remote Sensing |
container_volume |
36 |
container_issue |
1 |
container_start_page |
256 |
op_container_end_page |
264 |
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1778533354767908864 |