Scalable data-parallel implementations of object recognition using geometric hashing

Object recognition involves identifying known objects in a given scene. It plays a key role in image understanding. Geometric hashing has been proposed as a technique for model-based object recognition in occluded scenes. However, parallel techniques are needed to realize real time vision systems employing geometric hashing. In this paper, we present scalable parallel algorithms for object recognition using geometric hashing. We define a realistic abstract model of CM-5 in which explicit cost is associated with data routing and synchronization. We develop a load-balancing technique that results in scalable processor-time optimal algorithms for performing a probe on this model. Given a model of CM-5 with P PNs 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)|^1/3. On a mesh processor array of any size [formula] × [formula] which models MP-1, we show that a probe can be performed on O(|V(S)|/[formula]) time, log²|V(S)| ≤ P ≤ |V(S)|. These results do not assume any distributions of hash bin lengths or scene points. In earlier parallel implementations, the number of processors employed was independent of the size of the scene but depended on the size of the model database (which is usually very large). Our implementations on CM-5 and MP-1 significantly improve upon the number of processors employed and also result in superior time performance. The implementations developed in this paper require a number of processors that are independent of the size of the model database and are scalable with the machine size. Results of concurrent processing of multiple probes are also reported.