An image warping approach to spatio-temporal modelling
(2005) In Environmetrics 16(8). p.833-848- Abstract
- In this article we present a spatio-temporal dynamic model that can be realized using image warping. Image warping is a non-linear deformation which maps every point in one image plane to a point in another image plane. Using thin-plate splines, these deformations are defined by how a small set of points is mapped, making the method computationally tractable. In our case the dynamics of the process is modelled by thin-plate spline deformations and how they vary in time. Thus we make no assumption of stationarity in time. Finding the deformation between two images in the space-time series is a trade-off between a good match of the images and, a smooth, physically plausible, deformation. This is formulated as a penalized likelihood problem,... (More)
- In this article we present a spatio-temporal dynamic model that can be realized using image warping. Image warping is a non-linear deformation which maps every point in one image plane to a point in another image plane. Using thin-plate splines, these deformations are defined by how a small set of points is mapped, making the method computationally tractable. In our case the dynamics of the process is modelled by thin-plate spline deformations and how they vary in time. Thus we make no assumption of stationarity in time. Finding the deformation between two images in the space-time series is a trade-off between a good match of the images and, a smooth, physically plausible, deformation. This is formulated as a penalized likelihood problem, where the likelihood measures how good the match is and the penalty comes from a prior model on the deformation. The dynamic model we suggest can be used to make forecasts and also to estimate the uncertainties associated with these. An introduction to image warping and thin-plate splines is given as well as an application where the methodology is applied to the problem of nowcasting radar precipitation. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/210767
- author
- Åberg, Sofia LU ; Lindgren, Finn LU ; Malmberg, Anders LU ; Holst, Jan LU and Holst, Ulla LU
- organization
- publishing date
- 2005
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- dynamic models, image warping, thin-plate, forecasting, splines, spatio-temporal modelling
- in
- Environmetrics
- volume
- 16
- issue
- 8
- pages
- 833 - 848
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- wos:000234031900004
- scopus:28944446703
- ISSN
- 1099-095X
- DOI
- 10.1002/env.741
- language
- English
- LU publication?
- yes
- id
- 404d844c-2729-4866-94e2-6b9b6908c482 (old id 210767)
- date added to LUP
- 2016-04-01 12:21:36
- date last changed
- 2022-01-27 02:43:03
@article{404d844c-2729-4866-94e2-6b9b6908c482,
abstract = {{In this article we present a spatio-temporal dynamic model that can be realized using image warping. Image warping is a non-linear deformation which maps every point in one image plane to a point in another image plane. Using thin-plate splines, these deformations are defined by how a small set of points is mapped, making the method computationally tractable. In our case the dynamics of the process is modelled by thin-plate spline deformations and how they vary in time. Thus we make no assumption of stationarity in time. Finding the deformation between two images in the space-time series is a trade-off between a good match of the images and, a smooth, physically plausible, deformation. This is formulated as a penalized likelihood problem, where the likelihood measures how good the match is and the penalty comes from a prior model on the deformation. The dynamic model we suggest can be used to make forecasts and also to estimate the uncertainties associated with these. An introduction to image warping and thin-plate splines is given as well as an application where the methodology is applied to the problem of nowcasting radar precipitation.}},
author = {{Åberg, Sofia and Lindgren, Finn and Malmberg, Anders and Holst, Jan and Holst, Ulla}},
issn = {{1099-095X}},
keywords = {{dynamic models; image warping; thin-plate; forecasting; splines; spatio-temporal modelling}},
language = {{eng}},
number = {{8}},
pages = {{833--848}},
publisher = {{John Wiley & Sons Inc.}},
series = {{Environmetrics}},
title = {{An image warping approach to spatio-temporal modelling}},
url = {{http://dx.doi.org/10.1002/env.741}},
doi = {{10.1002/env.741}},
volume = {{16}},
year = {{2005}},
}