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Proceedings Paper

Correlation filter fusion for detection: morphological, wavelet, and Gabor methods
Author(s): David P. Casasent; John Scott Smokelin; Anqi Ye; Roland H. Schaefer
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Paper Abstract

We consider the detection of candidate objects (regions of interest) in a scene containing high clutter, multiple objects in different classes, independent of aspect view, with hot/cold/bimodal/partial object variations, and with low contrast targets. We use three different filters with each designed to produce high probability of detection (PD). We fuse the results from different outputs to reduce false alarms (PFA). All filters are realizable on a correlator.

Paper Details

Date Published: 9 November 1993
PDF: 11 pages
Proc. SPIE 2026, Photonics for Processors, Neural Networks, and Memories, (9 November 1993); doi: 10.1117/12.163562
Show Author Affiliations
David P. Casasent, Carnegie Mellon Univ. (United States)
John Scott Smokelin, Carnegie Mellon Univ. (United States)
Anqi Ye, Carnegie Mellon Univ. (United States)
Roland H. Schaefer, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 2026:
Photonics for Processors, Neural Networks, and Memories
Stephen T. Kowel; William J. Miceli; Joseph L. Horner; Bahram Javidi; Stephen T. Kowel; William J. Miceli, Editor(s)

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