Multiple shape recognition using pairwise geometric histogram based algorithms

Pairwise geometric histogram (PGH) based algorithms have previously been shown to be a robust solution for the recognition of arbitrary 2D shapes in the presence of occlusion and scene clutter (Evans et al., 1993). The method is both statistically founded and complete in the sense that a shape may be reconstructed from its PGH representation (Riocreuz et al., 1994). The generality of this method has been further reinforced by an analysis of its scaleability which concludes that, if used appropriately, it is suitable for the recognition of very large numbers of objects (Ashbrook et al., 1995). The present authors demonstrate the application of PGHs to recognition tasks involving very large model training sets