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

Piet Bijl; Frank L. Kooi; Maarten A. Hogervorst

doi:10.1117/12.2067454

20 October 2014 A TOD dataset to validate human observer models for target acquisition modeling and objective sensor performance testing

Piet Bijl, Frank L. Kooi, Maarten A. Hogervorst

Author Affiliations +

Proceedings Volume 9249, Electro-Optical and Infrared Systems: Technology and Applications XI; 92490R (2014) https://doi.org/10.1117/12.2067454
Event: SPIE Security + Defence, 2014, Amsterdam, Netherlands

Abstract

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.

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Piet Bijl, Frank L. Kooi, and Maarten A. Hogervorst "A TOD dataset to validate human observer models for target acquisition modeling and objective sensor performance testing", Proc. SPIE 9249, Electro-Optical and Infrared Systems: Technology and Applications XI, 92490R (20 October 2014); https://doi.org/10.1117/12.2067454

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KEYWORDS

Sensors

Electro optical modeling

Eye

Performance modeling

Visual process modeling

Data modeling

Sensor performance

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