Using a Hopfield network for rotation and scale independent pattern recognition
Thomas L. Hemminger, C.A. Pomalaza Raez · 2002
This paper outlines a two-dimensional pattern recognition paradigm which is independent of rotation and scale. It is based on the Hopfield neural network, using a configuration similar to that employed for the traveling salesman problem. Enhancements to the original Hopfield design are included based on the eigenvalues of the connection matrix and through extensive simulations. The goal is to determine the underlying linear transformation between a binary valued test image and an unknown input pattern by minimizing the energy within the network. Experiment has demonstrated that this unconventional scheme performs successfully on a variety of rotated and scaled images and is robust against additive noise.>