Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Face Authentication Test on the BANCA Database
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Maplets for correspondence-based object recognition
Neural Networks - 2004 Special issue: New developments in self-organizing systems
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Neurocomputing
Rapid convergence to feature layer correspondences
Neural Computation
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IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
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ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
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We present a neural network model that learns to find correspondences. The network uses control units that gate feature information from an input to a model layer of neural feature detectors. The control units communicate via a network of lateral connections to coordinate the gating of feature information such that information about spatial feature arrangements can be used for correspondence finding. Using synaptic plasticity to modify the connections amongst control units, we show that the network can learn to find the relevant neighborhood relationship of features in a given class of input patterns. In numerical simulations we show quantitative results on pairs of one-dimensional artificial inputs and preliminary results on two-dimensional natural images. In both cases the system gradually learns the structure of feature neighborhood relations and uses this information to gradually improve in the task of correspondence finding.