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Hybrid Object Models: Combining Symbolic and Subsymbolic Object Recognition Strategies

By Ulrich Büker and Heinz Nixdorf

Abstract

In this paper, we describe a hybrid object recognition system. The integration of biologically motivated subsymbolic image recognition and of symbolic reasoning and control mechanisms shows a rapid increase in performance, well above of each single module in the system. Starting with an holistic subsymbolic recognition system we added an AI-based symbolic recognition layer on top of our previous system. We could show that hereby the recognition of occluded objects in an industrial manufacturing environment is possible. Furthermore, the system was enhanced to the recognition of complex 3D objects by using a multiple view approach. A detailed description of the AI layer of the system will be given. Some examples of object models will clarify our strategies. Keywords: Object Recognition, Artificial Intelligence, Neural Networks, Active Vision, Hybrid Vision Systems 1 Introduction The recognition of 3D objects is surely one of the most challenging goals in computer vision and is much ha..

Topics: Object Recognition, Artificial Intelligence, Neural Networks, Active Vision, Hybrid Vision Systems
Year: 1998
OAI identifier: oai:CiteSeerX.psu:10.1.1.41.9207
Provided by: CiteSeerX
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