We consider the optimal likelihood algorithm for the estimation of
a target location when the images are corrupted by substitutive
noise. We show the relationship between the optimal algorithm and
the sliced orthogonal nonlinear generalized (SONG)
correlation. The SONG correlation is based on the application of a
linear correlation to corresponding binary slices of both the input
scene and the reference object with appropriate weight factors. For
a particular case, we show that the optimal strategy is a function of
only the number of pixels for which the gray values in the noisy image
match the ones of the reference image when the substitutive noise is
uniformly distributed. This is exactly what a particular definition
of the SONG correlation does.
© 2001 Optical Society of America
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