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Proceedings Paper

Joint space/spatial-frequency representations as preprocessing steps for neural nets; joint recognition of separately learned patterns; results and limitations
Author(s): Manfred Rueff; P. Frankhauser; Frank Dettki
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Paper 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

Paper Details

Date Published: 1 February 1991
PDF: 16 pages
Proc. SPIE 1382, Intelligent Robots and Computer Vision IX: Neural, Biological, and 3D Methods, (1 February 1991); doi: 10.1117/12.25217
Show Author Affiliations
Manfred Rueff, Fraunhofer-Institute for Manufacturing Engineering and Autom (Germany)
P. Frankhauser, Fraunhofer-Institute for Manufacturing Engineering and Autom (Germany)
Frank Dettki, Fraunhofer-Institute for Manufacturing Engineering and Autom (Germany)


Published in SPIE Proceedings Vol. 1382:
Intelligent Robots and Computer Vision IX: Neural, Biological, and 3D Methods
David P. Casasent, Editor(s)

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