Imperfection Patterns Research Articles

Signals representing coefficients in a series approximation to the two-dimensional density function represented by a pattern are directly measured by focusing an image of the pattern on a small number of weighted masks with a photocell behind each, or alternatively by passing the image of the pattern past a small number of weighted slits. The functions weighting the masks or slits are determined so as to enable subsequent normalization of the signals to make them independent of the incidentals of the configuration of the pattern; such incidentals may include variations in position, density, scale, orientation, and viewing perspective. Finally, recognition of a pattern is achieved by comparing the normalized signal values for an unknown pattern with sets of stored values representing known patterns; a “best match” of such values provides a basis for recognition of imperfect patterns in the presence of noise and measurement error. An example of one application of the method is discussed, as are the results of a computer simulation of a character reader based on the application.