<title>Real-time model-based vision for industrial domains</title>

Abstract

This paper describes a model based vision system that has been developed which is able to perform model based reasoning at real-time (or near real-time) rates and for which both the hardware and prototyping costs are low. The basic approach taken is to extract a set of useful features from observed models using a library of feature primitive operators. Scale and orientation invariant combinations of these features are used as indices into a hardware lookup table to establish initial correspondence between similar combinations that will be encountered when examining unknown objects. When performing initial recognition of an unknown object, evidence for an object in a particular spatial pose is accumulated, giving rise to an initial set of hypotheses. The strongest hypotheses are then refined by iteratively hypothesizing new (previously uninstantiated) model/object feature matches and computing a confidence measure associated with the current instantiation set. If confidence increases the newly hypothesized instantiation is retained, otherwise it is discarded.