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Proceedings Paper

Image coding using a knowledge-based recognition system
Author(s): Mohamed L. Hambaba; Bryan P. Coffey; Nanik R. Khemlani
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Paper Abstract

The basic idea of the system proposed in this paper lies in the fact that an image usually includes areas of different significance, so we have to code them in a different way to reach an accurate reproduction. Our system divides an image into areas of various importance which we code using wavelet transformations and neural networks for knowledge-based recognition. In this paper, we explain how the functional relationship between intensity and spatial frequency at the limits of human perception in vision (Contrast Sensitivity Threshold (CST) Curve) can guide one to choose the norm of the error metrics, the compression level in the wavelet hierarchy, and the coefficient quantization strategies to minimize the human perception of error. The CST curve is learned by a backpropagation neural network.

Paper Details

Date Published: 16 September 1992
PDF: 4 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140019
Show Author Affiliations
Mohamed L. Hambaba, Stevens Institute of Technology (United States)
Bryan P. Coffey, Stevens Institute of Technology (United States)
Nanik R. Khemlani, Stevens Institute of Technology (United States)

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

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