Parameterisation invariant statistical shape models

Johan Karlsson, Anders Ericsson, Karl Åström

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review

8 Citations (SciVal)


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
Original languageEnglish
Title of host publicationProceedings of the 17th International Conference on Pattern Recognition
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)0-7695-2128-2
Publication statusPublished - 2004
Event17th International Conference on Pattern Recognition, 2004 - Cambridge, England, Cambridge, United Kingdom
Duration: 2004 Aug 232004 Aug 26


Conference17th International Conference on Pattern Recognition, 2004
Country/TerritoryUnited Kingdom

Subject classification (UKÄ)

  • Mathematics


  • automatic shape modelling
  • invariant statistical shape models
  • covariance matrix
  • curve parametrisations


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