Improving Specificity in PDMs using a Hierarchical Approach

The Point Distribution Model PDM has proved useful for many tasks involving the location and tracking of deformable objects A principal limitation is non speci city in constructing a model to in clude all valid object shapes the inclusion of some invalid shapes is unavoidable due to the linear nature of the approach Bregler and Omohundro describe a piecewise linear method for applying constraints within model shape space whereby principal component analysis is used on training data clusters in shape space to generate lower dimensional overlapping subspaces Object shapes are constrained to lie within the union of these subspaces thus improving the speci city of the model This is an important development in itself but its most useful qual ity is that it lends itself to automated training Manual annotation of training examples has previously been necessary to ensure good speci city in PDMs requiring expertise and time and thus limiting the amount of training data that can feasibly be collected The use of shape space constraints means that such accurate annotation is unnec essary and automated training becomes signi cantly more successful In this paper we expand on Bregler and Omohundro s work sug gesting an alternative representation for the linear pieces and showing how a two level hierarchy in shape space can be used to improve e ciency and reduce noise We perform an evaluation on both synthetic and automatically trained real models