Constraint propagation neural networks for Huffman-Clowes scene labeling

The authors propose a three-layered constraint satisfaction neural network to perform Huffman-Clowes scene labeling. Given a line drawing the network establishes a consistent labeling for all the edges or detects that it is physically unrealizable. Experimental results show that this approach exploits the parallel architecture inherent in the network and is faster than the conventional algorithmic method.<<ETX>>