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( jV (S)j 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 jV (S)j 1 3 . On a mesh processor array of size p P..