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Object recognition by a Hopfield neural network

机译:Hopfield神经网络的对象识别

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摘要

A model-based recognition method is introduced which is formulated as an optimization problem. An energy function is derived which represents the constraints on the best solution in order to find the best match. A two-dimensional binary Hopfield neural network is implemented to minimize the energy function. The state of each neuron in the Hopfield network represents the possibility of a match between a node in the model graph and a node in the scene graph.
机译:介绍了一种基于模型的识别方法,其被制定为优化问题。导出能量函数,它代表了最佳解决方案的约束,以便找到最佳匹配。实现了二维二进制Hopfield神经网络以最小化能量函数。 Hopfield网络中的每个神经元的状态表示模型图中的节点与场景图中的节点之间的匹配的可能性。

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