COMPARISON OF SOME METHODS FOR PROCESSING 'GREY LEVEL' DATA IN WEIGHTLESS NETWORKS

Weightless neural networks have been used in pattern recognition vision systems for many years. The operation of these networks requires that binary values be produced from the input data, and the simplest method of achieving this is to generate a logic '1' if a given sample from the input data exceeds some threshold value, and a logic '0' otherwise. If, however, the lighting of the scene being observed changes, then the input data 'appears' very different. Various methods have been proposed to overcome this problem, but so far there have been no detailed comparisons of these methods indicating their relative performance and practicalities. In this chapter the results are given of some initial tests of the different methods using real world data.