Polyhedral Object Recognition with Sparse Data

recognition of polyhedral objects with sparse data, has been developed and implemented on a distributed array processor, the AMT DAP 500, which operates in SIMD mode. Measurements involving the location vectors and the surface normals at m data points, considered in pairs, are compared with the corresponding maximum and minimum values associated with nxn pairs of object model faces, in a process that exploits nxn parallelism. The overall processing time is essentially proportional to mx(m-l)/2, to explore the interpretation tree to its full depth. This paper discusses the nature of the comparisons between object models and data, together with the need to make these comparisons in a particular sequence, and results of test runs with a variety of object models and different geometric constraints are presented herein. Comparison is made with the corresponding sequential process, and with the more costly method of Flynn and Harris, in which n m processing elements are required to achieve, at best, a processing time of the same order of magnitude