Russell J. Hewett
A phase field method for tomographic reconstruction from limited data
Hewett, Russell J.; Jermyn, Ian; Heath, Michael T.; Kamalabadi, Farzad; Bowden, Richard; Collomosse, John; Mikolajczyk, Krystian
Authors
Professor Ian Jermyn i.h.jermyn@durham.ac.uk
Professor
Michael T. Heath
Farzad Kamalabadi
Richard Bowden
John Collomosse
Krystian Mikolajczyk
Abstract
Classical tomographic reconstruction methods fail for problems in which there is extreme temporal and spatial sparsity in the measured data. Reconstruction of coronal mass ejections (CMEs), a space weather phenomenon with potential negative effects on the Earth, is one such problem. However, the topological complexity of CMEs renders recent limited data reconstruction methods inapplicable. We propose an energy function, based on a phase field level set framework, for the joint segmentation and tomographic reconstruction of CMEs from measurements acquired by coronagraphs, a type of solar telescope. Our phase field model deals easily with complex topologies, and is more robust than classical methods when the data are very sparse. We use a fast variational algorithm that combines the finite element method with a trust region variant of Newton’s method to minimize the energy. We compare the results obtained with our model to classical regularized tomography for synthetic CME-like images.
Citation
Hewett, R. J., Jermyn, I., Heath, M. T., Kamalabadi, F., Bowden, R., Collomosse, J., & Mikolajczyk, K. (2012). A phase field method for tomographic reconstruction from limited data. In Proceedings of the British Machine Vision Conference (1-11). https://doi.org/10.5244/c.26.120
Conference Name | British Machine Vision Conference 2012 (BMVC) |
---|---|
Conference Location | Guildford, Surrey |
Start Date | Sep 3, 2012 |
End Date | Sep 7, 2012 |
Publication Date | Sep 1, 2012 |
Deposit Date | Jul 27, 2015 |
Publicly Available Date | Apr 12, 2016 |
Pages | 1-11 |
Book Title | Proceedings of the British Machine Vision Conference |
DOI | https://doi.org/10.5244/c.26.120 |
Files
Published Conference Proceeding
(713 Kb)
PDF
Copyright Statement
© 2012. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.
You might also like
Modality-Constrained Density Estimation via Deformable Templates
(2021)
Journal Article
Assessing the Non-Uniqueness of a Well Test Interpretation Model Using a Bayesian Approach
(2020)
Conference Proceeding