Multiresolution Stochastic Hybrid Shape Models with Fractal Priors . The multiresolution nature of the wavelet decomposition allows for a smooth transition between the global superquadric and the local descriptors. This is done by incor porating prior statistics to constrain the variation of the parameters of the elastic model. For complex shapes described by a large number of often highly correlated parameters, the use of such priors may become tedious. Multiresolution Stochastic Hybrid Shape Models with Fractal Priors .
Subdivision surfaces allow the modeling of surfaces with arbitrary topological type.
Wavelets provide a natural means for efficiently and uniquely decomposing a complex shape into a concise representation. Vemuri and Radisavljevic. Their method is capable of modeling topological cylinders at various resolutions, and they also describe a technique for fitting shapes to sampled medical data. B. C. Vemuri and A. Radisavljevic. Multiresolution stochastic hybrid shape models with fractal priors .