Model-Based Object Recognition Using The Connection Machine

This paper reports on a model-based object recognition system and its parallel implementation on the Connection Machine' System. The goal is to be able to recognize a large number of partially occluded, two-dimensional objects in scenes of moderate complexity. In contrast to traditional approaches, the system described here uses a parallel hypothesize and test method that avoids serial search. The basis for hypothesis generation is provided by local boundary features (such as corners formed by intersecting line segments) that constrain an object's position and orientation. Once generated, hypothetical instances of models are either accepted or rejected by a verification process that computes each instance's overall confidence. Even on a massively parallel computer, however, the potential for combinatorial explosion of hypotheses is still of major concern when the number of objects and models becomes large. We control this explosion by accumulating weak evidence in the form of votes in position and orientation space cast by each hypothesis. The density of votes in parameter space is expected to be proportional to the degree to which hypotheses receive support from different local features. Thus, it becomes possible to rank hypotheses prior to verification and test more likely hypotheses first.