Invariant recognition of 2-D objects using Alopex neural networks
Kootala P. Venugopal, Abhijit S. Pandya, Raghavan Sudhakar · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
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 handwritten 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.