Using the Particle Filter Approach to Building Partial Correspondences Between Shapes
Constructing correspondences between points characterizing one shape with those characterizing another is crucial to understanding what the two shapes have in common. These correspondences are the basis for most alignment processes and shape similarity measures. In this paper we use particle filters to establish perceptually correct correspondences between point sets characterizing shapes. Local shape feature descriptors are used to establish the probability that a point on one shape corresponds to a point on the other shape. Global correspondence structures are calculated using additional constraints on domain knowledge. Domain knowledge is characterized by prior distributions which serve to characterize hypotheses about the global relationships between shapes. These hypotheses are formulated online. This means global constraints are learnt during the particle filtering process, which makes the approach especially interesting for applications where global constraints are hard to define a priori. As an example for such a case, experiments demonstrate the performance of our approach on partial shape matching.
particle filters shape matching shape recognition feature correspondences shape alignment