Non-linear generalization of point distribution models using polynomial regression

Abstract We have previously described how to model shape variability by means of point distribution models (PDM) in which there is a linear relationship between a set of shape parameters and the positions of points on the shape. This linear formulation can fail for shapes which articulate or bend. We show examples of such failure for both real and synthetic classes of shape. A new, more general formulation for PDMs, based on polynomial regression, is presented. The resulting polynomial regression PDMs (PRPDM) perform well on the data for which the linear method failed.

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