The purpose of this paper is to test correlation methods for pattern recognition applications. A broad overview of the
main correlation architectures is first given. Many correlation data are compared with those obtained from standard
pattern recognition methods. We used our simulations to predict improved decisional performance from correlation
methods. More specifically, we are focused on the POF filter and composite filter family. We present an optimized
composite correlation filter, called asymmetric segmented phase-only filter (ASPOF) for mobile target recognition
applications. The main objective is to find a compromise between the number of references to be merged in the
correlation filter and the time needed for making a decision. We suggest an all-numerical implementation of a
VanderLugt (VLC) type composite filter. The aim of this all-numerical implementation is to take advantage of the
benefits of the correlation methods and make the correlator easily reconfigurable for various scenarios. The use of
numerical implementation of the optical Fourier transform improves the decisional performance of the correlator.
Further, it renders the correlator less sensitive to the saturation phenomenon caused by the increased number of
references used for fabricating the composite filter. Different tests are presented making use of the peak-to-correlation
energy criterion and ROC curves. These tests confirm the validity ofour technique. Elderly fall detection and underwater
mine detection are two applications which are considered for illustrating the benefits of our approach. The present work
is motivated by the need for detailed discussions of the choice of the correlation architecture for these specific
applications, pre-processing in the input plane and post processing in the output plane techniques for such analysis.