DocumentCode
276645
Title
Interpreting line drawings with higher order neural networks
Author
Salem, Gaby J. ; Young, Tzay Y.
Author_Institution
IBM Corp., Boca Raton, FL, USA
Volume
i
fYear
1991
fDate
8-14 Jul 1991
Firstpage
713
Abstract
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
Keywords
computer vision; neural nets; Hopfield-type network; analog model; constraints; higher order neural networks; line drawing interpretation; line labeling; quadratic energy model; trihedral scenes; updating equation; Application software; Artificial intelligence; Artificial neural networks; Computer vision; Engineering drawings; Equations; Labeling; Layout; Neural networks; Neurons;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
Conference_Location
Seattle, WA
Print_ISBN
0-7803-0164-1
Type
conf
DOI
10.1109/IJCNN.1991.155268
Filename
155268
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