A framework for statistical 3-D object recognition
In this contribution we describe an object-oriented software architecture for image segmentation, 3-D pose estimation as well as Bayesian object recognition: models are represented by densities, model generation corresponds to parameter estimation tasks, and the identification applies the Bayesian decision rule. We show results of 3-D object recognition experiments based on the observation of 2-D points or lines. (C) 1997 Elsevier Science B.V.
object-oriented programming statistical object recognition