Invariant feature matching by Hopfield-type neural network

In this paper, a novel Hopfield model for silhouette matching invariant to projective transformations is proposed. Although the new network has higher-order energy function, we show that it can be solved using a standard second-order Hopfield network, by taking advantage of the neighborhood information in the data. The experimental results with real data show that the proposed method can provide accurate matching results in image registration and object recognition