A rotation, scaling and translation invariant pattern classification system

Abstract

Presents a hybrid pattern classification system which can classify patterns in a rotation, scaling, and translation invariant manner. The system is based on preprocessing the input image to map it into a rotation, scaling, and translation invariant canonical form, which is then classified by a multilayer feedforward neural net. Results from a number of classification problems are also presented in the paper.<>