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

Stephen D. Burks; Joshua M. Doe; Brian P. Teaney

doi:10.1117/12.2178149

12 May 2015 Validating machine vision MRT performance against trained observer performance for linear shift invariant sensors

Stephen D. Burks, Joshua M. Doe, Brian P. Teaney

Author Affiliations +

Proceedings Volume 9452, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVI; 945204 (2015) https://doi.org/10.1117/12.2178149
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

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.

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Stephen D. Burks, Joshua M. Doe, and Brian P. Teaney "Validating machine vision MRT performance against trained observer performance for linear shift invariant sensors", Proc. SPIE 9452, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVI, 945204 (12 May 2015); https://doi.org/10.1117/12.2178149

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KEYWORDS

Sensors

Black bodies

Imaging systems

Modulation transfer functions

Machine vision

Temperature metrology

Contrast transfer function

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