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TOD to TTP calibration

Attachments

Author: Bijl, P. · Reynolds, J.P. · Vos, W.K. · Hogervorst, M.A. · Fanning, J.D.
Type:article
Date:2011
Source:Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXII, 26 April 2011 through 28 April 2011, Orlando, FL, 8014, 80140L-1 - 80140L-12
series:
Proceedings of SPIE - The International Society for Optical Engineering
Identifier: 431858
doi: doi:10.1117/12.887219
ISBN: 9780819485885
Article number: 80140L
Keywords: Vision · Identification · Perception · Range prediction · Target acquisition · Test method · TOD · TTP · Human · PCS - Perceptual and Cognitive Systems · BSS - Behavioural and Societal Sciences

Abstract

The TTP (Targeting Task Performance) metric, developed at NVESD, is the current standard US Army model to predict EO/IR Target Acquisition performance. This model however does not have a corresponding lab or field test to empirically assess the performance of a camera system. The TOD (Triangle Orientation Discrimination) method, developed at TNO in The Netherlands, provides such a measurement. In this study, we make a direct comparison between TOD performance for a range of sensors and the extensive historical US observer performance database built to develop and calibrate the TTP metric. The US perception data were collected doing an identification task by military personnel on a standard 12 target, 12 aspect tactical vehicle image set that was processed through simulated sensors for which the most fundamental sensor parameters such as blur, sampling, spatial and temporal noise were varied. In the present study, we measured TOD sensor performance using exactly the same sensors processing a set of TOD triangle test patterns. The study shows that good overall agreement is obtained when the ratio between target characteristic size and TOD test pattern size at threshold equals 6.3. Note that this number is purely based on empirical data without any intermediate modeling. The calibration of the TOD to the TTP is highly beneficial to the sensor modeling and testing community for a variety of reasons. These include: i) a connection between requirement specification and acceptance testing, and ii) a very efficient method to quickly validate or extend the TTP range prediction model to new systems and tasks. © 2011 SPIE.