<title>Grouping-based recognition system</title>

Abstract

A model-based computer vision system that can recognize three-dimensional objects from unknown viewpoints in single gray-scale images is presented. The system is based on an off-line model preprocessing stage, where a 3D recognition-oriented model and a strategy hierarchy are automatically generated. This strategy hierarchy provides the representation of associations between features detected bottom-up and the data base of object models, enabling the on-line recognition algorithm to be particularly efficient by reducing the recognition to a 2D-matching process. In order to perform an efficient indexing of the model data base (base level of the hierarchy), feature groupings based on the phenomenon of Perceptual Organization are used. Those groupings and structures in the image are likely to be invariant over a wide range of viewpoints. Since an initial estimate for the object and its viewpoint is found, a process of spatial correspondence is performed. This process brings the projections of 3D-models into direct correspondence with the 2D-image, solving the unknown viewpoint and model parameters.