Abstract
We have described previously a method of automatically constructing statistical models of shape. The method treats model-building as an optimisation problem by re-parameterising each shape so as to minimise the description length of the training set. The approach requires an explicit parameterisation of each shape, which is straightforward in 2D, but non-trivial in 3D. It is necessary to provide some parameterisation of the training set to initialise the optimisation. An inappropriate initial parameterisation can cause the optimisation to converge at a slower rate or stop it from converging to a satisfactory solution. In this paper we describe a method of producing a consistent parameterisation for a given set of surfaces. The consistent parameterisations were used to initialise the model-building algorithm and produced results that were significantly better than alternative approaches. © 2006 IEEE.
| Original language | English |
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| Title of host publication | 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings|IEEE Int. Symp. Biomed. Imag. Nano Macro Proc. |
| Publisher | IEEE |
| Pages | 1388-1391 |
| Number of pages | 3 |
| Volume | 2006 |
| ISBN (Print) | 0780395778, 9780780395770 |
| Publication status | Published - 2006 |
| Event | 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Arlington, VA Duration: 1 Jul 2006 → … |
Conference
| Conference | 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro |
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| City | Arlington, VA |
| Period | 1/07/06 → … |