Automatic Landmark Generation for Point Distribution Models

Point Distribution Models (PDMs) are statistically derived flexible templates which are trained on sets of examples of the object(s) to be modelled. They require that each example is represented by a set of points (landmaiks) and that each landmark represents the same location on each of the examples. Generating the landmarks from 2D boundaries or 3D surfaces has previously been a manual process. Here, we describe a method for automatically generating PDMs from a training set of pixel- lated boundaries in 2D. The algorithm is a two-stage process in which a pair-wise corresponder is first used to establish an approximate set of landmarks on each of the example boundaries; in the second phase the landmarks are refined using an iterative non-linear optimisation scheme to generate a more compact PDM. We present results for two objects - the right hand and a chamber of the heart. The mo- dels generated using the automatically placed landmarks are shown to be better than those derived from landmarks located manually.