Using geometric hashing with information theoretic clustering for fast recognition from a large CAD modelbase

We introduce a geometric hashing strategy to recognize CAD models from an organized hierarchy. Unlike most prior work in hashing using graph theoretic models, this work is a step closer to the classical, point based geometric hashing scheme. The geometric hashing strategy is used along with the hierarchical organization strategy defined by K. Sengupta and K.L. Boyer (1995). The combination of these two concepts can potentially reduce the recognition time considerably, especially versus the normal graph theoretic ideas, while retaining all of their benefits. We also present an error analysis of the hashing scheme considering the sensor noise and the scene clutter. Experiments with a CAD modelbase and both synthetic and real images indicate the potential of this scheme for fast recognition from large modelbases