The Fukunaga–Koontz (F–K) transform is a linear transformation
that performs image-feature extraction for a two-class image classification
problem. It has the property that the most important basis functions for
representing one class of image data (in a least-squares sense) are also the
least important for representing a second image class. We present a new method
of calculating the F–K basis functions for large dimensional imagery by
using a small digital computer, when the intraclass variation can be
approximated by correlation matrices of low rank. Having calculated the
F–K basis functions, we use a coherent optical processor to obtain the
coefficients of the F–K transform in parallel. Finally, these
coefficients are detected electronically, and a classification is performed by
the small digital computer.
© 1982 Optical Society of America
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