The application of security technology ranges from production control over supervision of special areas or objects to pattern recognition. In a lot of cases the security system deals as a preprocessor and its output should help the human visual system to detect important information. The output of hardcopy devices like printers or fax-machines is often restricted to quantized levels, so that a quantization process has to be executed. We present several attempts to perform this by the use of neural structures. The ability of layer networks and their learning algorithms lead to feedback networks. Our examination analyses the relationship between the theory of the feedback networks (especially the Hopfield net and the bidirectional associative memory net) and the iterative algorithms used in digital halftoning. This analysis allows a better understanding of the methods for digital halftoning and shows how they can benefit from each other.
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