Pattern recognition with variable discrimination capability by dual non-linear optical correlation

Abstract

The discrimination capability requirements of pattern recognition systems may vary from one given purpose to another. In this work a recognition system with variable and selective discrimination capability is obtained by applying a dual non-linear correlation (DNC) model to a joint-transform correlator. DNC is obtained by means of two non-linear operators that are applied to both the reference and input channels. A particular DNC is given by the values taken by two real control parameters that determine the non-linear operators. In comparison with conventional filtering methods, an increased and variable discrimination capability is achieved by varying the parameters values. Thus, variable tolerances are introduced in the recognition process. Specifically, tolerances to slight shape variations and intensity variations of the objects (alphabetic characters) are analysed in this work. Ranges for the two control parameters are found in each case in order to achieve either an increase or a relaxation in the system's discrimination capability. The developed application is extended to colour pattern recognition by multichannel correlation. In this case, four further applications with selective discrimination capability are developed: pattern recognition with high discrimination capability for shape variations and some tolerance to colour variations and, vice versa, pattern recognition with high discrimination capability for colour variations and some tolerance to slight shape variations; pattern recognition with high discrimination for both shape and colour, and, finally, a tolerance to slight variations in both shape and colour.