Automated Pivot Location for the Cartesian-Polar Hybrid Point Distribution Model

The Point Distribution Model (PDM) has already proved useful for many tasks involving the location or tracking of deformable objects. A principal limitation is that non-linear variations must be approximated by combining linear variations, which sometimes results in a non-optimal model producing implausible object shapes. The Cartesian-Polar Hybrid PDM helps to overcome this limitation; selective use of polar geometry allows bending or pivotal deformation to be modelled more accurately; model components which exhibit no such trend remain in the Cartesian domain. Use of the Hybrid PDM currently requires the identification of pivot points by hand. In this paper we present a method for automatically identifying rigid model parts and pivot points from the training data. Experimental results are given for real data from human hands and for synthetic data from a simple jointed object.