Abstract:
:Groupwise nonrigid image registration is a powerful tool to automatically establish correspondences across sets of images. Such correspondences are widely used for constructing statistical models of shape and appearance. As existing techniques usually treat registration as an optimization problem, a good initialization is required. Although the standard initialization-affine transformation-generally works well, it is often inadequate when registering images of complex structures. In this paper we present a more sophisticated method that uses the sparse matches of a parts+geometry model as the initialization. We show that both the model and its matches can be automatically obtained, and that the matches are able to effectively initialize a groupwise nonrigid registration algorithm, leading to accurate dense correspondences. We also show that the dense mesh models constructed during the groupwise registration process can be used to accurately annotate new images. We demonstrate the efficacy of the approach on three datasets of increasing difficulty, and report on a detailed quantitative evaluation of its performance.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Zhang P,Cootes TFdoi
10.1109/TMI.2011.2169077subject
Has Abstractpub_date
2012-02-01 00:00:00pages
341-58issue
2eissn
0278-0062issn
1558-254Xjournal_volume
31pub_type
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