Polyhedral object recognition with sparse data — validation of interpretations

Abstract

The method of Grimson, Lozano Pérez et al., for the generation of feasible interpretations of scenes with sparse data, has been developed and implemented by the authors on a distributed array processor, the AMT DAP, 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 maximum and minimum values associated with the n × n pairs faces of a polyhedral object model, in a process that exploits n × n parallelism. The subsequent validation of the interpretations, in which data points have been assigned provisionally to object model faces, are discussed.