ForeSight: fast object recognition using geometric hashing with edge-triple features

We present a new method for the recognition of polyhedral objects from 2D images based on geometric hashing. Rather than the point-based approach of previous geometric hashing implementations, which tend to be rather sensitive to image noise and spurious data, our method is based on triples of connected edges. As well as improving the robustness of the system, the use of higher level feature groupings results in a very efficient specialisation of the geometric hashing paradigm. Theoretical analyses of the ForeSight method show that it is more than ten times as fast as a comparable point-based geometric hashing implementation, while using only one-quarter of the memory. These results were confirmed by practical experiments on a database of 50 real images, in which the recognition rate achieved by ForeSight approached twice that of the conventional method