Title :
Interpreting line drawings with higher order neural networks
Author :
Salem, Gaby J. ; Young, Tzay Y.
Author_Institution :
IBM Corp., Boca Raton, FL, USA
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;
Conference_Titel :
Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-0164-1
DOI :
10.1109/IJCNN.1991.155268
