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Proceedings Paper

Finite-state neural networks for dimensionality reduction and smooth signal reconstruction
Author(s): Guoping Qiu; Martin R. Varley
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Paper Abstract

A finite-state auto-associative MLP neural network is studied in the context of dimensionality reduction and smooth signal reconstruction. We describe the structure and the training procedure of the finite-state network. One of the desirable properties of the auto-associative MLP is that the variance of the hidden units' outputs can be arranged in a descending order, so that efficient coding of the hidden layer output can be implemented. We provide experimental results to demonstrate that the finite-state network retains this desirable property of its memory-less counterpart. One of the application areas of the auto-associative MLP is image compression. As with other block based image compression techniques, this method cannot avoid the problem of annoying 'blocking effects' in the reconstructed images. We present simulation results to demonstrate that the finite-state auto-associative MLP can be used to achieve effective image data compression while significantly reducing the blocking effects.

Paper Details

Date Published: 4 March 1996
PDF: 11 pages
Proc. SPIE 2664, Applications of Artificial Neural Networks in Image Processing, (4 March 1996); doi: 10.1117/12.234253
Show Author Affiliations
Guoping Qiu, Univ. of Derby (United Kingdom)
Martin R. Varley, Univ. of Central Lancashire (United Kingdom)


Published in SPIE Proceedings Vol. 2664:
Applications of Artificial Neural Networks in Image Processing
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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