Guaranteed geometric hashing

Geometric hashing is an invariant feature-driven approach to model-based object recognition. Previous interest has focused on its ability to accommodate sensor error. This paper presents an enhancement of the geometric hashing technique which guarantees, under only a few constraints, that models will not be missed due to sensor noise. The authors´ geometric hashing algorithm enters model affine invariants into hash table regions defined by an exact error model, brings together known optimizations (table symmetry and the use of more than 3 model-scene point correspondences) and uses novel data organization. Experimental results (on both synthetic and real data) suggest that the authors´ modifications to a geometric hashing recognition scheme effectively overcome sensor noise.