Interpreting line drawings with higher order neural networks

Interpreting line drawings with higher order neural networks

A neural network solution to line labeling is presented. Line labeling constraints in trihedral scenes are designed into a Hopfield-type network. The labeling constraints require a higher-order of interaction than that of Hopfield and Tank's (1985) quadratic energy model. The analog model is mo...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: Salem, G.J., Young, T.Y.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Application software
Artificial intelligence
Artificial neural networks
Computer vision
Engineering drawings
Equations
Labeling
Layout
Neural networks
Neurons
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Beschreibung
Zusammenfassung:A neural network solution to line labeling is presented. Line labeling constraints in trihedral scenes are designed into a Hopfield-type network. The labeling constraints require a higher-order of interaction than that of Hopfield and Tank's (1985) quadratic energy model. The analog model is modified to include an additional layer of neurons. A brief introduction to the line labeling problem is provided. The design of the energy function and the updating equation is described. Experimental results are analyzed.< >
DOI:10.1109/IJCNN.1991.155268