Maplets for correspondence-based object recognition
Neural Networks - 2004 Special issue: New developments in self-organizing systems
A Bidirectional Hetero-Associative Memory for True-Color Patterns
Neural Processing Letters
A New Associative Model with Dynamical Synapses
Neural Processing Letters
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The goal of the visual correspondence problem is to establish a connectivity pattern (a mapping) between two images such that features projected from the same scene point are connected. Dynamic link matching (DLM) is a self-organizing dynamical system to establish such connectivity patterns for object recognition, but with rather naturally given simple interactions between pattern elements, its organizing process is slow. Here we propose to stabilize (store) established mappings so that they can be recovered efficiently and reliably in the future. This is implemented by modifying the underlying system of interactions using the established mappings as learning examples, where the Hebbian rule makes the adapted interactions proportional to the weights of an associative memory of these mappings. It is shown in simulation that the adapted interactions lead to faster and more robust DLM.