Neural networks: translation-, rotation- and scale-invariant pattern recognition
- 7 August 1988
- journal article
- Published by IOP Publishing in Journal of Physics A: General Physics
- Vol. 21 (15) , L783-L787
- https://doi.org/10.1088/0305-4470/21/15/007
Abstract
A neural network model which is capable of recognising transformed versions of a set of learnt patterns is proposed. The group of transformations includes global translations, rotations and scale transformations. The neural firing thresholds are used as additional degrees of freedom.Keywords
This publication has 14 references indexed in Scilit:
- An Exactly Solvable Asymmetric Neural Network ModelEurophysics Letters, 1987
- THE AUGMENTED MODELS OF ASSOCIATIVE MEMORY ASYMMETRIC INTERACTION AND HIERARCHY OF PATTERNSInternational Journal of Modern Physics B, 1987
- Hierarchical model of memoryPhysica A: Statistical Mechanics and its Applications, 1986
- Storing and Retrieving Information in a Layered Spin SystemEurophysics Letters, 1986
- 'Ordered' spin glass: a hierarchical memory machineJournal of Physics C: Solid State Physics, 1985
- Storing Infinite Numbers of Patterns in a Spin-Glass Model of Neural NetworksPhysical Review Letters, 1985
- Neurons with graded response have collective computational properties like those of two-state neurons.Proceedings of the National Academy of Sciences, 1984
- ‘Unlearning’ has a stabilizing effect in collective memoriesNature, 1983
- Neural networks and physical systems with emergent collective computational abilities.Proceedings of the National Academy of Sciences, 1982
- The existence of persistent states in the brainMathematical Biosciences, 1974