A concavity based algorithm for the recognition of partially occulded 3-dimensional objects

A new method for viewer independent recognition of occluded, complex 3-dimensional objects, independent of rotation, translation and a practical range of scale factors, is presented. The basis of this technique is a set of points referred to as critical points. These points are derived from a structure known as the concavity tree. The concavity tree is a unique representation for planar shapes. It is shown that the set of critical points derived from the concavity tree, although not unique, will generally retain enough shape information to distinguish one shape from another. The critical point set is a small subset of the set of points which form the tree. The procedure requires several projections for any particular object; therefore several sets of critical points are required for each object. Shapes are compared and identified on the basis of shape (feature) vectors formed on the critical point sets.