MODEL-DRIVEN INTERPRETATION IN INTELLIGENT VISION SYSTEMS

With a constructive, knowledge-based theory of perception as its foundation, this paper starts vith a review and critique of some artificial intelligence programs that purport to see. It is then argued that these computer programs for scene analysis offer the hope of providing a more adequate account of human competence in interpreting line drawings as polyhedra than do the current psychological theories. This thesis has several aspects. The one emphasized here is that those programs have explored a variety of methods of incorporating a priori knowledge of objects through the use of models. After outlining the range of models used, presenting a set of criteria for evaluating the use of model information and sketching some psychological theories, the various proposals are contrasted. This discussion leads to two new proposals for exploiting model information that involve elaborations of an existing program, POLY.