Parametrization of closed surfaces for 3-D shape description
Computer Vision and Image Understanding
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
3D Statistical Shape Models Using Direct Optimisation of Description Length
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Spherical parametrization and remeshing
ACM SIGGRAPH 2003 Papers
3D active shape models using gradient descent optimization of description length
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Deformable templates using large deformation kinematics
IEEE Transactions on Image Processing
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Determining groupwise correspondence across a set of unla-belled examples of either shapes or images, by the use of an optimisation procedure, is a well-established technique that has been shown to produce quantitatively better models than other approaches. However, the computational cost of the optimisation is high, leading to long convergence times. In this paper, we show how topologically non-trivial shapes can be mapped to regular grids (called shape images). This leads to an initial reduction in computational complexity. By also considering the question of regularisation, we show that a non-parametric fluid regulariser can be applied in a principled manner, the fluid flowing on the shape surface itself, whilst not loosing the computational gain made by the use of shape images. We show that this non-parametric regularisation leads to a further considerable gain, when compared to previous parametric regularisation methods. Quantitative evaluation is performed on biological datasets, and shown to yield a substantial decrease in convergence time, with no loss of model quality.