Nonlinear pattern recognition correlators based on color-encoding single-channel systems.

In color pattern recognition, color channels are normally processed separately and afterward the correlation outputs are combined. This is the definition of multichannel processing. We combine a single-channel method with nonlinear filtering based on nonlinear correlations. These nonlinear correlations yield better discrimination than common matched filtering. The method codes color information as amplitude and phase distributions and is followed by correlations related to binary decompositions. The technique is based on binary decompositions of the red, green, and blue and the hue, saturation, and intensity monochromatic channels of the reference and of the input scene, after which the binary information on the red, green, and blue channels and that of the hue, saturation, and intensity channels are encoded as different angles of a phase distribution. We have applied the method to images degraded by high levels of substitutive noise. Results show that the sliced orthogonal nonlinear generalized correlation detects the target with a high degree of discrimination when other methods fail.