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Parameterisation invariant statistical shape models

Karlsson, Johan LU ; Ericsson, Anders LU and Åström, Karl LU (2004) 17th International Conference on Pattern Recognition, 2004 In Proceedings of the 17th International Conference on Pattern Recognition p.23-26
Abstract
In this paper novel theory to automate shape modelling is described. The main idea is to develop a theory that is intrinsically defined for curves, as opposed to a finite sample of points along the curves. The major problem here is to define shape variation in a way that is invariant to curve parametrisations. Instead of representing continuous curves using landmarks, the problem is treated analytically and numerical approximations are introduced at the latest stage. The problem is solved by calculating the covariance matrix of the shapes using a scalar product that is invariant to global reparametrisations. An algorithm for implementing the ideas is proposed and compared to a state of the an algorithm for automatic shape modelling. The... (More)
In this paper novel theory to automate shape modelling is described. The main idea is to develop a theory that is intrinsically defined for curves, as opposed to a finite sample of points along the curves. The major problem here is to define shape variation in a way that is invariant to curve parametrisations. Instead of representing continuous curves using landmarks, the problem is treated analytically and numerical approximations are introduced at the latest stage. The problem is solved by calculating the covariance matrix of the shapes using a scalar product that is invariant to global reparametrisations. An algorithm for implementing the ideas is proposed and compared to a state of the an algorithm for automatic shape modelling. The problems with instability in earlier formulations are solved and the resulting models are of higher quality (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
automatic shape modelling, invariant statistical shape models, covariance matrix, curve parametrisations
in
Proceedings of the 17th International Conference on Pattern Recognition
pages
23 - 26
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
17th International Conference on Pattern Recognition, 2004
external identifiers
  • wos:000223878400006
  • scopus:10044256280
ISBN
0-7695-2128-2
DOI
10.1109/ICPR.2004.1333696
language
English
LU publication?
yes
id
6996ca54-c0a1-4673-8de4-246cfb33d953 (old id 614646)
date added to LUP
2007-11-27 12:41:29
date last changed
2017-02-19 04:29:41
@inproceedings{6996ca54-c0a1-4673-8de4-246cfb33d953,
  abstract     = {In this paper novel theory to automate shape modelling is described. The main idea is to develop a theory that is intrinsically defined for curves, as opposed to a finite sample of points along the curves. The major problem here is to define shape variation in a way that is invariant to curve parametrisations. Instead of representing continuous curves using landmarks, the problem is treated analytically and numerical approximations are introduced at the latest stage. The problem is solved by calculating the covariance matrix of the shapes using a scalar product that is invariant to global reparametrisations. An algorithm for implementing the ideas is proposed and compared to a state of the an algorithm for automatic shape modelling. The problems with instability in earlier formulations are solved and the resulting models are of higher quality},
  author       = {Karlsson, Johan and Ericsson, Anders and Åström, Karl},
  booktitle    = {Proceedings of the 17th International Conference on Pattern Recognition},
  isbn         = {0-7695-2128-2},
  keyword      = {automatic shape modelling,invariant statistical shape models,covariance matrix,curve parametrisations},
  language     = {eng},
  pages        = {23--26},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Parameterisation invariant statistical shape models},
  url          = {http://dx.doi.org/10.1109/ICPR.2004.1333696},
  year         = {2004},
}