A new method for reliable pattern recognition of multiple distorted objects in a cluttered background and consequent
classification of the detected objects is proposed. The method is based on a bank of composite correlation filters. The
filters are designed with the help of an iterative algorithm exploiting a modified version of synthetic discriminant
functions. The bank consists of a minimal quantity of the filters required for a given input scene to guarantee a
prespecified value of discrimination capability for pattern recognition and classification of all objects. Statistical analysis
of the number of required correlations versus the recognition performance is provided and discussed. Computer
simulation results obtained with the proposed method are compared with those of known techniques in terms of
performance criteria for recognition and classification of objects.
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