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Proceedings Paper

Feature-based correlation filters for distortion invariance
Author(s): Samuel Peter Kozaitis; Robert Petrilak; Wesley E. Foor
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Paper 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.

Paper Details

Date Published: 1 July 1992
PDF: 10 pages
Proc. SPIE 1701, Optical Pattern Recognition III, (1 July 1992); doi: 10.1117/12.138334
Show Author Affiliations
Samuel Peter Kozaitis, Florida Institute of Technology (United States)
Robert Petrilak, Florida Institute of Technology (United States)
Wesley E. Foor, Rome Lab. (United States)

Published in SPIE Proceedings Vol. 1701:
Optical Pattern Recognition III
David P. Casasent; Tien-Hsin Chao, Editor(s)

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