Object recognition on a Systolic Array

Abstract

Computer vision systems for recognition include both the extraction of features and the matching of those features with a known model. Traditionally, the most time consuming step has been feature extraction, but new parallel architectures are removing the bottleneck at this level. Once features have been exuacted from an image considerable geometric search is still necessary to form relationships between the extracted features and to match those features and feature aggregates with a model. One can take advantage of certain constraints about the appearance of an object, but with complex images or multiple models intensive processing is still required. We have developed some algorithms for doing these geometric search operations in parallel on iWarp, a long linear array of VLSI processing elements currently being designed by Carnegie Mellon and Intel Corporation. We have simulated a system which uses these algorithms to do an object recognition task (after low-level vision) almost completely on a 72 processor iWarp array. An analysis of this system indicates a speedup by a factor of roughly 100 to 250 over a sequential version running on a VAX 8650.