Hypothesis Generation In A Theorem Proving Based Recognition System
Mitchell J. Nathan · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1985
Two mechanisms are presented which select viable candidates for a pattern matching process. This selection process is termed hypothesis generation. One mechanism is data-driven, using viewpoint dependent features to eliminate obviously poor choices and inhibit unlikely choices. The second mechanism is context-driven, using previously recognized objects as cues for generating future match candidates. These mechanisms have been incorporated into a theorem proving based pattern matching system and serve to constrain the space of possible matches. This isolates the system performance from the effects of the large search space that is necessary for a general-purpose vision system operating in an unconstrained environment.