Invariant recognition of 2D objects using Alopex neural networks

Kootala P. Venugopal; Abhijit S. Pandya; Raghavan Sudhakar

doi:10.1117/12.139995

16 September 1992 Invariant recognition of 2-D objects using Alopex neural networks

Kootala P. Venugopal, Abhijit S. Pandya, Raghavan Sudhakar

Proceedings Volume 1709, Applications of Artificial Neural Networks III; (1992) https://doi.org/10.1117/12.139995
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

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.

Citation Download Citation

Kootala P. Venugopal, Abhijit S. Pandya, and Raghavan Sudhakar "Invariant recognition of 2-D objects using Alopex neural networks", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); https://doi.org/10.1117/12.139995

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