Metrics for image-based modeling of target acquisition

Jonathan D. Fanning

doi:10.1117/12.921034

18 May 2012 Metrics for image-based modeling of target acquisition

Jonathan D. Fanning

Proceedings Volume 8355, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIII; 835514 (2012) https://doi.org/10.1117/12.921034
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

This paper presents an image-based system performance model. The image-based system model uses an image metric to compare a given degraded image of a target, as seen through the modeled system, to the set of possible targets in the target set. This is repeated for all possible targets to generate a confusion matrix. The confusion matrix is used to determine the probability of identifying a target from the target set when using a particular system in a particular set of conditions. The image metric used in the image-based model should correspond closely to human performance. The image-based model performance is compared to human perception data on Contrast Threshold Function (CTF) tests, naked eye Triangle Orientation Discrimination (TOD), and TOD including an infrared camera system. Image-based system performance modeling is useful because it allows modeling of arbitrary image processing. Modern camera systems include more complex image processing, much of which is nonlinear. Existing linear system models, such as the TTP metric model implemented in NVESD models such as NV-IPM, assume that the entire system is linear and shift invariant (LSI). The LSI assumption makes modeling nonlinear processes difficult, such as local area processing/contrast enhancement (LAP/LACE), turbulence reduction, and image fusion.

Citation Download Citation

Jonathan D. Fanning "Metrics for image-based modeling of target acquisition", Proc. SPIE 8355, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIII, 835514 (18 May 2012); https://doi.org/10.1117/12.921034

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