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A Bayesian approach for spatially adaptive regularisation in non-rigid registration

Abstract:

This paper introduces a novel method for inferring spatially varying regularisation in non-rigid registration. This is achieved through full Bayesian inference on a probabilistic registration model, where the prior on transformations is parametrised as a weighted mixture of spatially localised components. Such an approach has the advantage of allowing the registration to be more flexibly driven by the data than a more traditional global regularisation scheme, such as bending energy. The propo...

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Publisher copy:
10.1007/978-3-642-40763-5_2

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Institution:
University of Oxford
Department:
Oxford, MSD, Psychiatry
Role:
Author
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Journal:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume:
8150 LNCS
Issue:
PART 2
Pages:
10-18
Publication date:
2013-01-01
DOI:
EISSN:
1611-3349
ISSN:
0302-9743
URN:
uuid:d16759cb-3641-4ca9-8923-009b7190b590
Source identifiers:
436772
Local pid:
pubs:436772
Language:
English

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