Feature-based correlation filters for distortion invariance

Samuel Peter Kozaitis; Robert Petrilak; Wesley E. Foor

doi:10.1117/12.138334

1 July 1992 Feature-based correlation filters for distortion invariance

Samuel Peter Kozaitis, Robert Petrilak, Wesley E. Foor

Proceedings Volume 1701, Optical Pattern Recognition III; (1992) https://doi.org/10.1117/12.138334
Event: Aerospace Sensing, 1992, Orlando, FL, United States

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 than a synthetic discriminant function (SDF) filter. This was done by creating a filter that retained the invariant features of a training set. By simulation, our feature-based filter offered a range of performance by setting a parameter to different values. As the value of the parameter was changed, correlation peaks within the training set became more consistent and broader. 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 SDF filter.

Citation Download Citation

Samuel Peter Kozaitis, Robert Petrilak, and Wesley E. Foor "Feature-based correlation filters for distortion invariance", Proc. SPIE 1701, Optical Pattern Recognition III, (1 July 1992); https://doi.org/10.1117/12.138334

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