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SIAM Journal on Imaging Sciences
Article . 2013 . Peer-reviewed
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SIAM Journal on Imaging Sciences
Article . 2013 . Peer-reviewed
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Geometric PCA of Images

descriptionPublicationkeyboard_double_arrow_right Article 01 Jan 2013 English Publisher:Society for Industrial & Applied Mathematics (SIAM)Journal:SIAM Journal on Imaging Sciences, volume 6, pages 1,851-1,879 (eissn: 1936-4954, Copyright policy )
Authors: Bigot, Jérémie; Gouet, Raúl; López, Alfredo;

doi: 10.1137/120864556

handle: 10533/148235 , 10533/135302

Geometric PCA of Images

Abstract

We describe a method for analyzing the shape variability of images, called geometric PCA. Our approach is based on the use of deformation operators to model the geometric variability of images around a reference mean pattern. This leads to a new algorithm for estimating shape variability. Some numerical experiments on real images are proposed to highlight the benefits of this approach. The consistency of this procedure is also analyzed in statistical deformable models.

Related Organizations
Keywords

Consistent estimation, Principal component analysis, Geometric variability, Physique mathématique, Fréchet mean, Deformable models, Mean pattern estimation, Image registration

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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