Abstract

While diffraction pattern sampling has been widely used in the classification of patterns, its usage has been somewhat limited by the need to devise rather sophisticated algorithms. We describe sorting or classifying a variety of patterns using the ring-wedge photodetector and commercially available neural network software. With this combination, it is no longer necessary to write specialized software, and experiments which previously might have taken 4-6 months are now completed in 4 h or less. Three separate experiments are described quantitatively. First, thumbprints are sorted for eight individuals using six prints from each person. A learning set of four prints is shown to give excellent results and both ring and wedge data are used for the input neuron signals. Second, as an experiment in particulate analysis, colloidal suspensions of polyvinyltoluene spheres have been measured over a concentration of 256-1. Finally, automatic image quality assessment is described using a variety of input imagery. Widespread new uses are predicted for this hybrid system that combines the ring-wedge photodetector and the neural network.

© 1989 Optical Society of America