1994 Special Issue: A fast dynamic link matching algorithm for invariant pattern recognition

Authors:
Wolfgang K. Konen;Thomas Maurer;Christoph von der Malsburg
Affiliations:
Institut für Neuroinformatik, Ruhr-Universität Bochum, Germany;Institut für Neuroinformatik, Ruhr-Universität Bochum, Germany;Institut für Neuroinformatik, Ruhr-Universität Bochum, Germany and Department of Computer Science, University of Southern California, USA
Venue:
Neural Networks - Special issue: models of neurodynamics and behavior
Year:
1994

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Abstract

When recognizing patterns or objects, our visual system can easily separate what kind of pattern is seen and where (location and orientation) it is seen. Neural networks as pattern recognizers can deal well with noisy input patterns, but have difficulties when confronted with the large variety of possible geometric transformations of an object. We propose a flexible neural mechanism for invariant recognition based on correlated neuronal activity and the self-organization of dynamic links. The system can deal in parallel with different kinds of invariances such as translation, rotation, mirror-reflection, and distortion. It is shown analytically that parts of the neuronal activity equations can be replaced by a faster, but functionally equivalent, algorithmic approach. We derive a measure based on the correlation of activity which allows an unsupervised decision of whether a given input pattern matches with a stored model pattern (''what''-part). At the same time, the dynamic links specify a flexible mapping between input and model (''where''-part). In simulations, the system is applied to both artificial input data and grey level images of real objects.