Reconstruction of Patterns from Noisy Inputs Using Morphological Associative Memories
Journal of Mathematical Imaging and Vision
Novel robust stability criteria for stochastic hopfield neural networks with time delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ACC'09 Proceedings of the 2009 conference on American Control Conference
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Hopfield neural network image matching based on hausdorff distance and chaos optimizing
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
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The affine transformation, which consists of rotation, translation, scaling, and shearing transformations, can be considered as an approximation to the perspective transformation. Therefore, it is very important to find an effective means for establishing point correspondences under affine transformation in many applications. In this paper, we consider the point correspondence problem as a subgraph matching problem and develop an energy formulation for affine invariant matching by a Hopfield type neural network. The fourth-order network is investigated first, then order reduction is done by incorporating the neighborhood information in the data. Thus we can use the second-order Hopfield network to perform subgraph isomorphism invariant to affine transformation, which can be applied to an affine invariant shape recognition problem. Experimental results show the effectiveness and efficiency of the proposed method