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A TOD dataset to validate human observer models for target acquisition modeling and objective sensor performance testing

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Author: Bijl, P. · Kooi, F.L. · Hogervorst, M.A.
Publisher: SPIE
Source:Huckridge, D.A.Ebert, R., Electro-Optical and Infrared Systems: Technology and Applications XI
Proceedings of SPIE - The International Society for Optical Engineering
Identifier: 523216
doi: doi:10.1117/12.2067454
ISBN: 9781628413120
Article number: 92490R
Keywords: Vision · Human observer · Range performance · Sensor · Target Acquisition · TOD · Triangle orientation discrimination · Computer vision · Infrared devices · Optical systems · Sensors · Statistical tests · Human observers · Range performance · Target acquisition · TOD · Triangle orientation discriminations · Benchmarking · Human Performances · PCS - Perceptual and Cognitive Systems · ELSS - Earth, Life and Social Sciences


End-to-end Electro-Optical system performance tests such as TOD, MRTD and MTDP require the effort of several trained human observers, each performing a series of visual judgments on the displayed output of the system. This significantly contributes to the costs of sensor testing. Currently, several synthetic human observer models exist that can replace real human observers in the TOD sensor performance test and can be used in a TOD based Target Acquisition (TA) model. The reliability that may be expected with such a model is of key importance. In order to systematically test HVS (Human Vision System) models for automated TOD sensor performance testing, two general sets of human observer TOD threshold data were collected. The first set contains TOD data for the unaided human eye. The second set was collected on imagery processed with sensor effects, systematically varying primary sensor parameters such as diffraction blur, pixel pitch, and spatial noise. The set can easily be extended to other sensor effects including dynamic noise, boost, E-zoom, or fused sensor imagery and may serve as a benchmark for competing human vision and sensor performance models.