Polyhedral Object Recognition with Sparse Data - Validation of Interpretations
David S. Holder, Hilary Buxton · 1989
Abstract The method of Grimson, Lozano Perez 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.