Homomorphic graph matching using self-organising Hopfield network

In the past, the Hopfield network has been employed to solve pattern recognition problems by subgraph isomorphism which naturally constrains the scene to have at most one occurrence of any object model. Recently, the author proposed a novel programming procedure to generate a homomorphic mapping which enables simultaneous recognition of multiple instances of any particular object model in the scene (P.N. Suganthan, 1995; 1995). However, in order to generate the desired homomorphic mapping, a number of parameters have to be fine tuned. A self-organising Hopfield network is introduced that learns the constraint parameters using a Liapunov indirect method based learning approach.