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Analysis and Characterization of Pattern Classifiers

Award Information
Agency: Department of Defense
Branch: Army
Contract: DAAD19-02-C-0067
Agency Tracking Number: 44157-CI
Amount: $100,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N/A
Solicitation Number: N/A
Timeline
Solicitation Year: N/A
Award Year: 2002
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
6785 Hollister Avenue
Goleta, CA 93117
United States
DUNS: 153927827
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Gary Key
 Principal Investigator
 (321) 277-8396
 garykey@aol.com
Business Contact
 Linda Sparks
Title: Manager, Contracts and Legal
Phone: (805) 685-6672
Email: lsparks@fti-net.com
Research Institution
 University of Florida
 Brian Lane
 
516 Weil Hall
Gainesville, FL 32611
United States

 (352) 392-1092
 Nonprofit College or University
Abstract

"Frontier Technology, Inc. (FTI) and University of Florida (UF) propose to develop designs for automatically-generated statistical pattern recognition systems (GASPs) that can classify uncooperative targets among time-varying natural and manmadebackgrounds. We also propose to analyze the performance of the envisioned GASPs to:(a) covertly acquire feature data (e.g., statistical, spectral, and spatial cues) from target/background imagery,(b) apply multiple classifiers to target/background information to select probable target location and identity,(c) apply inferencing rules to disambiguate infeasible or contra-dictory classifier outputs.Pattern selection, key to successful system operation in mission- and threat-specific scenarios, will utilize Dempster-Schaefer the-ory and UF's powerful data fusion paradigm, Morphological Neural Nets (MNN).Phase-I will evaluate, extend and exploit FTI and UF's success-ful, DoD-sponsored R&D for dynamic pattern recognition and ATR to develop and test an efficient system design for target classifier output fusion and disambiguation. System design will includeanalysis of complexity and cost of potential hardware implementa-tions. In a Phase-II effort, we will use Phase-I results to drive candidate pattern downselection in FTI's DoD-supported TNE para-digm. MNNs and TNE have been proven highly successful in awide va-riety of recognition problems, thus we propose to analyze GASP sys-tem performance in realistic ATR scenarios. If successful, the proposed research will constitute a breakthrough in the solution of problems related to automatic target classifica-tion and pattern recognition. These problems occur extensively throughout both the military and commercial sectors: thepotential payoff is high. FTI will license or sell the solution to large aerospace companies for military applications. We plan to partner with a commercial company involved in law enforcement technologies, environmental monitoring and drug enforcementtechniques.C. KEYWORDSAdaptive Pattern Recognition, Neural nets (NN's), Morpholo

* Information listed above is at the time of submission. *

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