Orthogonal subspaces for correlation masking

Abstract

A digital correlation mask that induces orthogonality among a prescribed set of reference imagery is described. In this particular implementation, the resulting correlation is undersampled and shift-variant, though if it is applied to optical correlators, those limitations are removed. A method of introducing orthogonality among the weights among the training set from which filter values are obtained is derived, so that the correlation value from a given filter is representative of the unique nature of the reference object as compared against the other objects in the training class.