A neural network position independent multiple pattern recogniser

This paper describes a neural network model for computer vision which has position invariant properties. The network is designed to form part of a more comprehensive vision system. The purpose of the network is to classify features in a position independent manner and retain the spatial relationship...

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Bibliographic Details
Published in:Artificial intelligence in engineering 1996, Vol.10 (2), p.117-126
Main Authors: Grimes, C., Picton, P.D., Elliman, D.G.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
computer vision
Connectionism. Neural networks
Exact sciences and technology
neural networks
pattern recognition
Pattern recognition. Digital image processing. Computational geometry
Citations: Items that this one cites
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Description
Summary:This paper describes a neural network model for computer vision which has position invariant properties. The network is designed to form part of a more comprehensive vision system. The purpose of the network is to classify features in a position independent manner and retain the spatial relationship between detected features. Inherent parallelism in the network allows multiple features to be simultaneously classified with the spatial relationships preserved.
ISSN:0954-1810
DOI:10.1016/0954-1810(95)00021-6