Joint space/spatial-frequency representations as preprocessing steps for neural nets; joint recognition of separately learned patterns; results and limitations

Manfred Rueff; P. Frankhauser; Frank Dettki

doi:10.1117/12.25217

1 February 1991 Joint space/spatial-frequency representations as preprocessing steps for neural nets; joint recognition of separately learned patterns; results and limitations

Manfred Rueff, P. Frankhauser, Frank Dettki

Author Affiliations +

Proceedings Volume 1382, Intelligent Robots and Computer Vision IX: Neural, Biological, and 3D Methods; (1991) https://doi.org/10.1117/12.25217
Event: Advances in Intelligent Robotics Systems, 1990, Boston, MA, United States

Abstract

There is an increasing demand for fast and reliable pattern recog nition methods in many fields of industry in particular in inspec tion. Conventional pattern recognition systems mostly are not ca pable to cope with such tasks. Neural nets seem to be well suited for most of the requirements. Preprocessing steps to reduce the number of neurons in cognitive units are essential in applying neural paradigms to vision. Joint space/ spatialfrequency representations are discussed in view of their application to such image preprocessing. A system is proposed consisting of a low level and a cognitive unit of the Hopfield type. Experimental results reached with a simulation of this system are demonstrated. With the system the joint recognition of separately learned patterns is possible. SPIE Vol. 1382 Intelligent Robots and Computer Vision IX: Neural Biological and3-D Methods (1990) / 255

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Manfred Rueff, P. Frankhauser, and Frank Dettki "Joint space/spatial-frequency representations as preprocessing steps for neural nets; joint recognition of separately learned patterns; results and limitations", Proc. SPIE 1382, Intelligent Robots and Computer Vision IX: Neural, Biological, and 3D Methods, (1 February 1991); https://doi.org/10.1117/12.25217

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