Rapid object recognition from a large model database

A design for a system to perform object recognition from a large model data base is presented, focusing on efficient indexing. The authors propose a decision-theoretic approach using a Bayesian framework to achieve efficient indexing of model objects. A decision-theoretic measure of the discriminatory power of a feature for a model object is defined in terms of posterior probability. Domain-specific knowledge compiled off-line from CAD model data is used in order to estimate posterior probabilities that define the discriminatory power of features for model objects. Detectability of a feature defined as a function of the feature itself, viewpoint, sensor characteristics, and the feature detection algorithm(s) is also considered in the computation of discriminatory power. In order to speed up the indexing or selection of the correct objects, the authors generate and verify the object hypotheses for the features detected in the scene in the order of the discriminatory power of these features for model objects