Scalable data parallel object recognition using geometric hashing on CM-5

Presents scalable parallel algorithms for object recognition using geometric hashing. We define an abstract model of the CM-5 computer. We develop a load balancing technique that results in scalable processor-time optimal algorithms for performing a probe on the CM-5 model. Given a model of a CM-5 with P processor nodes and a set S of feature points in a scene, a probe of the recognition phase can be performed in O(|V(S)|)/P) time, where V(S) is the set of votes cast by feature points in S. This algorithm is scalable in the range 1⩽P⩽√[|V(S)|/log|V(S)|]. These results do not assume any distributions of hash bin lengths or scene points. The implementations developed in this paper require a number of processors which is independent of the size of the model database and which is scalable with the machine size