Abstract: In an optical correlator, binary phase-only filters (BPOFs) that recognize objects that vary in a nonrepeatable way are essential for recognizing objects from actual sensors. An approach is required that is as descriptive as a BPOF yet robust to object and background variations of an unknown or nonrepeatable type. We developed a BPOF that was more robust to unknown variations than a binary version of a synthetic discriminant function (fSDF) filter. We compared the values of spatial frequencies of a training set and compared them in terms of their similarity. Then, we grouped them into a cluster by forcing some values to zero. In this way, we retained the invariant spatial frequencies of a training set and generated a ternary filter. Our filter offered a range of performance by adjusting a parameter. At one extreme, our filter offered similar performance to that of a fSDF filter. As the value of the parameter was changed, correlation peaks within the training set became more consistent and broader as the filter became more robust. In addition, the feature-based filter was potentially useful for recognizing objects outside the training set. Furthermore, the feature-based filter was more easily calculated and trained than an fSDF filter.!8
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