Image coding using a knowledge-based recognition system

Mohamed L. Hambaba; Bryan P. Coffey; Nanik R. Khemlani

doi:10.1117/12.140019

16 September 1992 Image coding using a knowledge-based recognition system

Mohamed L. Hambaba, Bryan P. Coffey, Nanik R. Khemlani

Proceedings Volume 1709, Applications of Artificial Neural Networks III; (1992) https://doi.org/10.1117/12.140019
Event: Aerospace Sensing, 1992, Orlando, FL, United States

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.

Citation Download Citation

Mohamed L. Hambaba, Bryan P. Coffey, and Nanik R. Khemlani "Image coding using a knowledge-based recognition system", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); https://doi.org/10.1117/12.140019

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