A two-stage neural network for translation, rotation and size-invariant visual pattern recognition

A two-stage neural network is described for transformation-invariant visual pattern recognition. In the first stage, features are extracted after normalizing the image. It is shown how parameters of spatial transformation can be estimated even in the presence of noise by using knowledge about rigid objects. Circular arcs in the normalized image are used as generalized features to describe the input pattern. Each image pixel contributes to the features which it can constitute. Contributions from noisy pixels are distributed over the feature space, whereas meaningful parts contribute to clusters that correspond to features of the image. In the second stage, the image is classified on the basis of these features by a multilayer perceptron network trained using a backpropagation algorithm