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Proceedings Paper

Validating machine vision MRT performance against trained observer performance for linear shift invariant sensors
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Paper Abstract

Researchers at the US Army Night Vision and Electronic Sensors Directorate have added the functionality of Machine Vision MRT (MV-MRT) to the NVLabCap software package. While the original calculations of MV-MRT were compared to human observers performance using digital imagery in a previous effort,1 the technical approach was not tested on 8-bit imagery using a variety of sensors in a variety of gain and level settings. Now that it is more simple to determine the MV-MRT for a sensor in multiple gain settings, it is prudent to compare the results of MV-MRT in multiple gain settings to the performance of human observers for thermal imaging systems that are linear and shift invariant. Here, a comparison of the results for a LWIR system to trained human observers is presented.

Paper Details

Date Published: 12 May 2015
PDF: 8 pages
Proc. SPIE 9452, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVI, 945204 (12 May 2015); doi: 10.1117/12.2178149
Show Author Affiliations
Stephen D. Burks, U.S. Army Night Vision and Electronic Sensors Directorate (United States)
Joshua M. Doe, U.S. Army Night Vision and Electronic Sensors Directorate (United States)
Brian P. Teaney, U.S. Army Night Vision and Electronic Sensors Directorate (United States)


Published in SPIE Proceedings Vol. 9452:
Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVI
Gerald C. Holst; Keith A. Krapels, Editor(s)

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