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==Background==
|author = T. F. Cootes
|title = Statistical models of appearance for computer vision
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|year = 1995
|author1=D.H. Cooper |author2=T.F. Cootes |author3=C.J. Taylor |author4=J. Graham |issue = 61
}}</ref> and became a standard in [[computer vision]] for the [[statistical shape analysis|statistical study of shape]]<ref>{{
|title = Shape discrimination in the Hippocampus using an MDL Model
|year = 2003
|conference = IMPI
|url = http://www2.wiau.man.ac.uk/caws/Conferences/10/proceedings/8/papers/133/rhhd_ipmi03%2Epdf
|author = Rhodri H. Davies and Carole J. Twining and P. Daniel Allen and Tim F. Cootes and Chris J. Taylor
|access-date = 2007-07-27
|archive-url = https://web.archive.org/web/20081008194350/http://www2.wiau.man.ac.uk/caws/Conferences/10/proceedings/8/papers/133/rhhd_ipmi03%2Epdf
|archive-date = 2008-10-08
|url-status = dead
}}</ref> and for [[image segmentation|segmentation]] of [[medical imaging|medical images]]<ref name=taylor/> where shape priors really help interpretation of noisy and low-contrasted [[pixel]]s/[[voxel]]s. The latter point leads to [[active shape model]]s (ASM) and [[active appearance model]]s (AAM).
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Due to the PCA properties: eigenvectors are mutually [[orthogonal]], form a basis of the training set cloud in the shape space, and cross at the 0 in this space, which represents the mean shape. Also, PCA is a traditional way of fitting a closed ellipsoid to a Gaussian cloud of points (whatever their dimension): this suggests the concept of bounded variation.
The idea behind
==See also==
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|quote=Images, annotations and data reports are placed in the enclosed zip-file.
|author1=Stegmann, M. B. |author2=Gomez, D. D.
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==External links==
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