Hopfield network with constraint parameter adaptation for overlapped shape recognition

We propose an energy formulation for homomorphic graph matching by the Hopfield network and a Lyapunov indirect method-based learning approach to adaptively learn the constraint parameter in the energy function. The adaptation scheme eliminates the need to specify the constraint parameter empirically and generates valid and better quality mappings than the analog Hopfield network with a fixed constraint parameter. The proposed Hopfield network with constraint parameter adaptation is applied to match silhouette images of keys and results are presented.