Optical Filtering and Correlator Evaluation
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 unrepeatable type. We investigated the use of BPOFs to recognize objects undergoing unknown variations. In our study, we developed a BPOF that was more robust than other designs. This was done by creating a filter that retained the invariant features of a training set. Our feature-based filter offered a range of performance by setting a parameter to different values. At one extreme, the filter offered similar performance to that of a synthetic discriminant function (SDF) filter. 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. Pattern Recognition, Optical Correlation, Binary Phased-Only Filter.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 1992
- Accession Number
- ADA257163
Entities
People
- Samuel P. Kozaitis
Organizations
- Florida Institute of Technology