'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
Includes bibliographical references (page V-400).In this paper we develop a general multiscale stochastic object detection algorithm for use in an automated inspection application. Information from a CAD model is used to initialize the object model and guide the training phase of the algorithm. An object is represented as a stochastic tree, where each node of the tree is associated with one of the various object components used to locate and identify the part. During the training phase a number of model parameters are estimated from a set of training images, some of which are generated from the CAD model. The algorithm then uses a fast multiscale search strategy to locate and identify the subassemblies making up the object tree. We demonstrate the performance of the algorithm on a typical mechanical assembly.This work was supported by an AT&T Bell Laboratories PhD Scholarship, the NEC Corporation, National Science Foundation grant number MIP93-00560, and National Science Foundation grant number CDR 8803017 to the Engineering Research Center for Intelligent Manufacturing Systems
