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Proceedings Paper

Invariant recognition of 2D objects using Alopex neural networks
Author(s): Kootala P. Venugopal; Abhijit S. Pandya; Raghavan Sudhakar
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Paper 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.

Paper Details

Date Published: 16 September 1992
PDF: 9 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.139995
Show Author Affiliations
Kootala P. Venugopal, Florida Atlantic Univ. (United States)
Abhijit S. Pandya, Florida Atlantic Univ. (United States)
Raghavan Sudhakar, Florida Atlantic Univ. (United States)


Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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