Zhu, H., Tang, C., De Freitas, A. et al. (1 more author) (2020) A maximum likelihood approach to joint groupwise image registration and fusion by a Student-t mixture model. In: 2019 22th International Conference on Information Fusion (FUSION). 22nd International Conference on Information Fusion, 02-05 Jul 2019, Ottawa, Canada. IEEE ISBN 9781728118406
Abstract
In this paper, we propose a Student- t mixture model (SMM) to approximate the joint intensity scatter plot (JISP) of the groupwise images. The problem of joint groupwise image registration and fusion is considered as a maximum likelihood (ML) formulation. The parameters of registration and fusion are estimated simultaneously by an expectation maximization (EM) algorithm. To evaluate the performance of the proposed method, experiments on several types of multimodal images are performed. Comprehensive experiments demonstrate that the proposed approach has better performance than other methods.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | image registration; image fusion; Student-t mixture model; expectation maximization |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 06 Jun 2019 09:36 |
Last Modified: | 27 Feb 2021 01:38 |
Published Version: | https://ieeexplore.ieee.org/abstract/document/9011... |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:146913 |