Invariant recognition of 2-D objects using Alopex neural networks

摘要

We describe a neural network based recognition scheme for 2-D objects. The Fourier Descriptors of the object boundary are taken as the features and they form the input to the neural network. A multilayered perceptron architecture is used for the classification and a stochastic algorithm called Alopex, is used for the network learning. The scheme is invariant to translation, rotation and scale changes to the object. Taking isolated hand written digits as the input data set, we show that the presented scheme gives very high recognition accuracy. The recognition scheme, learning algorithm and simulation results are discussed in detail.