Nonlinear Enhancement of Visual Target Detection

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$99,977.00
Award Year:
2005
Program:
STTR
Phase:
Phase I
Contract:
FA9550-05-C-0159
Award Id:
73403
Agency Tracking Number:
F054-021-0135
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
1900 Kresswood Cir., Dayton, OH, 45429
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
127205214
Principal Investigator:
ThomasHangartner
President
(937) 293-4109
thangart@earthlink.net
Business Contact:
ThomasHangartner
President
(937) 293-4109
thangart@earthlink.net
Research Institute:
WRIGHT STATE UNIV.
Joseph Thomas
3640 Col. Glenn Hwy.
Dayton, OH, 45435
(937) 775-3336
Nonprofit college or university
Abstract
Object identification, quantification and localization are key goals in both military target detection and quantitative medical imaging. As in medical imaging, military images are often difficult to interpret due to noise, spatial resolution limits, changing background conditions and uncertainty about the materials being imaged. We propose to vastly improve the results from hyperspectral imaging systems by modeling the physics that governs surface reflectivity; using the model to predict reflectivity of a few selected materials; validating the reflectivity model by acquiring spectral signatures of the materials with a characterized and calibrated hyperspectral imaging sensor; identify appropriate image- processing techniques to correct for instrument limitationsl; and analyzing the input and output signal-to-noise ratio of the sensor data and use this information to simulate a stochastic resonance-like (fixed threshold) approach to object identification. At the end of Phase I of this project we will provide a well defined approach and software to characterize the reflection properties of any material under test; a well defined approach and software to characterize imaging sensors of interest in hyperspectral imaging; image analysis procedures and software/hardware implementation to reduce the hyperspectral data set; and image segmentation/fusion procedures and software/hardware implementation to identify objects of interest.

* information listed above is at the time of submission.

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