Parameterisation invariant statistical shape models
(2004) 17th International Conference on Pattern Recognition, 2004 In Proceedings of the 17th International Conference on Pattern Recognition p.2326 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:
http://lup.lub.lu.se/record/614646
 author
 Karlsson, Johan ^{LU} ; Ericsson, Anders ^{LU} and Åström, Karl ^{LU}
 organization
 publishing date
 2004
 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
 IEEEInstitute of Electrical and Electronics Engineers Inc.
 conference name
 17th International Conference on Pattern Recognition, 2004
 external identifiers

 WOS:000223878400006
 Scopus:10044256280
 ISBN
 0769521282
 DOI
 10.1109/ICPR.2004.1333696
 language
 English
 LU publication?
 yes
 id
 6996ca54c0a146738de4246cfb33d953 (old id 614646)
 date added to LUP
 20071127 12:41:29
 date last changed
 20161013 04:42:08
@misc{6996ca54c0a146738de4246cfb33d953, 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}, isbn = {0769521282}, keyword = {automatic shape modelling,invariant statistical shape models,covariance matrix,curve parametrisations}, language = {eng}, pages = {2326}, publisher = {ARRAY(0x7d88270)}, series = {Proceedings of the 17th International Conference on Pattern Recognition}, title = {Parameterisation invariant statistical shape models}, url = {http://dx.doi.org/10.1109/ICPR.2004.1333696}, year = {2004}, }