Binary image decomposition for intensity invariant optical nonlinear correlations

Two methods for intensity invariant pattern recognition based on the summation of correlations between multiple binarized orthogonal gray scale images are proposed. The sliced orthogonal nonlinear generalized correlation uses the internal gray scale informations of objects. If the level of illumination of an object changes, the internal gray scale information of the object itself is preserved, although it is shifted. The first method is based on the normalization of segmented targets, and the second deals with the whole input image without segmentation and without normalization. Computer experiments show that correlation peaks of equal intensity are obtained for true objects with different unequal illuminations, and that the methods are very good at rejecting false targets in the presence of correlated disjoint noise. Because the second method is based on multiple linear correlations, it can be implemented optically with a joint transform correlator.