A Second-Order Translation, Rotation and Scale Invariant Neural Network
Shelly D. D. Goggin, Kristina M. Johnson, Karl E. Gustafson · Neural Information Processing Systems · 1990
A second-order architecture is presented here for translation, rotation and scale invariant processing of 2-D images mapped to n input units. This new architecture has a complexity of O(n) weights as opposed to the O(n3) weights usually required for a third-order, rotation invariant architecture. The reduction in complexity is due to the use of discrete frequency information. Simulations show favorable comparisons to other neural network architectures.