Efficient PDM shape fitting using the Kalman filter

While the ability of point distribution models to model complex deformable shapes is highly attractive, recovering shape instances is difficult in images containing multiple occluded and occluding shapes located in background clutter. The standard local refinement approach employed within the literature relies on the availability of good initial estimates. A highly efficient search strategy is presented for generating all plausible initial solutions by embedding a Kalman filter in a breadth-first search algorithm to use candidate observations extracted from the image to update the shape parameters of shape hypotheses and constrain the position of subsequent observations.