Target identification performance as a function of low spatial frequency image content

Driggers, Ronald G.; Vollmerhausen, Richard H.; Krapels, Keith A.

doi:10.1117/1.1288362

Cited by 11 publications

(5 citation statements)

References 10 publications

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“…The data are fitted by a logarithmic line. The correlation between PID and identifiability is high, with an average correlation coeffient (R) of 0.91 (for the individual observers the correlation coefficients are respectively: 0.92 for observer MH, and 0.89 for observer PB) 1 . 2 , as a function of identifiability (in pixels) for the average over the two observers.…”

Section: Resultsmentioning

confidence: 99%

“…Here we operationally define target identifiability as the amount of Gaussian blur that is required to reduce the target signature to its identification threshold. The rationale for the choice of a low-pass signature degradation filter is the fact that all spatial frequencies contribute to target identification 1 . The Gaussian blurring proces is easy to implement.…”

Section: Introductionmentioning

confidence: 99%

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Conspicuity and identifiability: efficient calibration tools for synthetic imagery

Toet

Hogervorst

Bijl

2004

Targets and Backgrounds X: Characterization and Representation

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We argue that visual conspicuity and identifiability are two efficient task-related measures that can be deployed to calibrate synthetic imagery that is intended to be used for human visual search and detection tasks. The conspicuity of a target is operationally defined as the region around the center of the visual field where the target is capable to attract visual attention. Visual conspicuity predicts human visual search performance in realistic and military relevant complex scenario's. Conspicuity can easily and quickly be measured either in the field (in complex environments) or in the lab. This eliminates the need for costly and time consuming visual search experiments. The agreement between field and lab measurements implies that conspicuity can be used to validate synthetic imagery. Target identifiability is operationally defined as the amount of Gaussian blur that is required to reduce the target signature to its identification threshold. It is an efficient metric that can be used to gain insight into human identification performance without having to resort to elaborate and costly experiments. Identifiability is directly related to PID-performance, and is therefore well suited for comparing synthetic and realistic imagery. We conclude that synthetic imagery can be calibrated for human visual search and detection tasks by setting the conspicuity and identifiability of targets equal to those of their real world counterparts.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Conspicuity and identifiability: efficient calibration tools for synthetic imagery

Toet

Hogervorst

Bijl

2004

Targets and Backgrounds X: Characterization and Representation

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show abstract

“…One possible parameter that could be varied for this purpose is target contrast, which can be easily modified in most simulators. In addition, contrast is a target attribute that would be expected to change in the real-world [8], for instance as a consequence of atmospheric conditions, sun angle, or target camouflage. The data of Fig.…”

Section: Discussionmentioning

confidence: 99%

P‐36: Equating Simulated and Real‐World Imagery Using a Target Identification Criterion

Geri¹,

Akhtar

Eidman³

et al. 2007

Symp Digest of Tech Papers

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“…The Johnson criteria do not consider image noise levels, electronic circuits used for high-frequency amplification, digital filtering, or interpolation, which impact range performance in such systems. Therefore, the latest, complete and reliable Targeting Mission Performance Metric (TTP) model has been developed by the US Army Night Vision and Electronic Sensors Directorate since 2000 (Schmieder & Weathersby, 1983;Driggers et al, 2000;NATO Research and Technology Organisation, 2003;Moyer et al, 2004;2006;Vollmerhausen & Jacobs, 2004;Krapels et al, 2006;2008;Teaney et al, 2007;Vollmerhausen et al, 2010) and the NVThermIP software (U.S. Army Night Vision and Electronic Sensors Directorate, 2001) is based on this model. Based on the 2.7*N50, it was defined as V50 (Krapels et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Range Estimation Performance of FLIR with Field Requirements Criteria

Akin¹

2023

Gazi University Journal of Science Part A: Engineering and Innovation

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Thermal imaging performance depends on many variables, ranging from the properties of the imaged object to atmospheric transmittance and system parameters. After clarification of the functional needs in system design or procurement, system parameters of the design that can meet these needs should be determined. Diagnosis/recognition from a distance is one of the foremost of these needs. The following briefly introduces the Forward Looking Infrared (FLIR) systems, followed by explanations for calculating the theoretical diagnostic range. After the theoretical information, sample systems are given, and high-performance FLIR systems are presented. To accurately analyze, measure and predict the performance of FLIR systems, a model should calculate summary performance measures of the system in the form of Minimum Resolvable Temperature (MRT) and Modulation Transfer Function (MTF) between a target and its background and estimate range for a given scenario electro-optical required for the performance evaluation of the system. The accuracy of these calculations will ultimately determine the accuracy of the model by which the performance of the FLIR system is evaluated.

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Target identification performance as a function of low spatial frequency image content

Cited by 11 publications

References 10 publications

Conspicuity and identifiability: efficient calibration tools for synthetic imagery

Conspicuity and identifiability: efficient calibration tools for synthetic imagery

P‐36: Equating Simulated and Real‐World Imagery Using a Target Identification Criterion

Evaluation of Range Estimation Performance of FLIR with Field Requirements Criteria

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