Automatic construction of Shape Models from examples has been the focus of intense research during the last couple of years. These methods have proved to be useful for shape segmentation, tracking and shape understanding. In this paper novel theory to automate shape modelling is described. The theory is intrinsically defined for curves although curves are infinite dimensional objects. The theory is independent of parameterisation and affine transformations. We suggest a method for implementing the ideas and compare it to minimising the Description Length of the model (MDL). It turns out that the accuracy of the two methods is comparable. Both the MDL and our approach can get Stuck at local minima. Our algorithm is less computational expensive and relatively good solutions are obtained after a few iterations. The MDL is, however, better suited at fine-tuning the parameters given good initial estimates to the problem. It is shown that a combination of the two methods outperforms either on its own.
|Conference||9th International Conference on Computer Vision, IEEE|
|Period||2003/10/13 → 2003/10/16|
- Affine invariant deformable shape representation
- Description length of the model
- Shape variation