Title Parameterisation invariant statistical shape models
Authors Johan Karlsson, Anders Ericsson, Karl Åström
Alternative Location http://dx.doi.org/10.1109/I..., Restricted Access
Publication Proceedings of the 17th International Conference on Pattern Recognition
Year 2004
Pages 23 - 26
Document type Conference paper
Conference name Proceedings of the 17th International Conference on Pattern Recognition
Conference Date 23-26 Aug. 2004
Conference Location Cambridge, UK
Status Published
Quality controlled Yes
Language eng
Publisher IEEE Comput. Soc
Abstract English 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
Keywords automatic shape modelling, invariant statistical shape models, covariance matrix, curve parametrisations,
ISBN/ISSN/Other ISBN: 0-7695-2128-2

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