Verification of Hypothesized Matches in Model-Based Recognition.

摘要

In model-based recognition a number of ad hoc techniques are used to decide whether or not a match of data to a model is correct. Generally an empirically determined threshold is placed on the fraction of model features that must be match. In this paper we present a more rigorous approach in which the conditions under which to accept a matched are derived based on fundamental grounds. We obtain an expression that relates the probability of a match occurring at random to the reaction of model features that are accounted for by the match. This expression is a function of the number of model features, the number of image features, and a bound on the degree of sensor noise. One implication of our analysis is that a proper threshold for matching must vary with the number of model and data features. Thus, it is important to be able to set the threshold as a function of a particular matching problem, rather than setting a single threshold as a function of a particular matching problem, based on experimentation. We analyze some existing recognition systems and find that our method yields a threshold similiar to the ones determined empirically for these systems, providing evidence of the validity of the technique. (KR)