A neural network approach to the labeling of line drawings

A solution to the labeling of the drawings using a neural network approach is presented. Line-labeling constraints are designed into a modified Hopfield networks. The design of the energy function and the updating equation is described. The energy function includes higher order terms than in the usual quadratic Hopfield model to accommodate the higher-order interactions required by the labeling constraints. The physical model is modified accordingly. An additional layer of neurons is used to synthesize a realizable circuit. The resulting network combines the standard Hopfield-network neurons with neurons performing two- and three-way Boolean AND operations. Simulation of network behavior for various trihedral scenes produced successful results.<
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