Object Recognition Using Multi-Layer Hopfield Neural Network
STATE UNIV OF NEW YORK AT BUFFALO DEPT OF ELECTRICAL AND COMPUTER ENGINEERING
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An object recognition approach based on concurrent coarse-and-fine matching using a multi-layer Hopfield neural network is presented. The proposed network consists of several cascaded single layer Hopfield networks, each encoding object features at a distinct resolution, with bidirectional interconnections linking adjacent layers. The interconnection weights between nodes associating adjacent layers are structured to favor node pairs for which model translation and rotation, when viewed at the two corresponding resolutions, are consistent. This inter-layer feedback feature of the algorithm reinforces the usual intra-layer matching process in conventional single layer Hopfield nets in order to compute the model-object match which is most consistent across several resolution levels. The performance of the algorithm is demonstrated in cases of images containing single and multiple occluded objects. These results are compared with recognition results obtained using a single layer Hopfield network.
- Statistics and Probability