Validation of the new triangle orientation discrimination method and ACQUIRE model predictions using observer performance data for ship targets

Bijl, Piet; Valeton, J. M.

doi:10.1117/1.601701

Cited by 43 publications

(33 citation statements)

References 5 publications

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“…So it is implicitly assumed that the shape of the MRTD-ID, MRTD-Rec or MTDP curves matches that for real targets. Bijl and Valeton 7,8 showed that the TOD does not only predict the range differences between well-sampled and under-sampled imagers at high thermal contrasts, but also that the shape of the TOD curve (for a CCD camera) matches the contrast vs. range relationship for real targets very well.…”

Section: Introductionmentioning

confidence: 99%

“…This procedure is not only very similar to the real target acquisition process, it also yields results that are independent of the subjective decision criterion of the observer. The relationship with real target acquisition was validated in several experiments 7,8 . Recently a TOD sensor model 9 was developed that explicitly takes into account non-linear operations such as sampling.…”

Section: Introductionmentioning

confidence: 99%

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TOD, NVTherm, and TRM3 model calculations: a comparison

2002

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At the moment there are three potential models and associated measures to replace the FLIR92 model and the MRTD as the standard for sensor performance characterization and TA range predictions. These are: 1) the NVTherm model that calculates the MRTD-ID, 2) the TRM3 model that calculates the MTDP and 3) the TOD model that calculates the TOD. The three models are grossly different in theoretical approach. We ran the models for the same set of hypothetical cameras. As default sensor, we used a 'typical' under-sampled Focal Plane Array camera. Then, we independently varied the pre-and post-filter MTF's over a wide range while keeping the sampling frequency fixed so that the cameras ranged between well-sampled and highly undersampled. The differences in outcome are striking. For example, for a range of sensors around the default the TOD model predicts that performance is determined primarily by the sampling frequency, while NVTherm predicts that pre-an post-filter blur dominate in this region. The results with TRM3 are surprising: the model predicts that increasing image information content by applying microscan to the default sensor, decreases predicted TA performance. The origin lies in the use of the periodic four-bar test pattern and the definition of the MTDP. In the low contrast region, the MTDP and the TOD are similar but the MRTD-ID deviates from these two. The MTDP and MRTD-ID are different even in the well-sampled region where they both should be equivalent to the conventional MRTD. The study shows that the choice of a model has a large impact on sensor design decisions or trade-offs between sensors. The TOD method is the most general of the three approaches and has the strongest basis. Until now, validation of the model predictions is very limited. Therefore, a joint TA performance study with simulated sensors (e.g. those used in the present study) and target contrast as variables would be very useful.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TOD, NVTherm, and TRM3 model calculations: a comparison

2002

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“…That is, if a triangle and target have the same square root area, the probability of ensemble acquisition should vary together as a function of contrast. Bijl and Valeton (1998b) validated the TOD metric against the cycles-on-target metric in the ACQUIRE model. ACQUIRE is used to predict the acquisition range for targets of a particular size and contrast.…”

Section: Other Issues Related To the Johnson Criteriamentioning

confidence: 99%

Soldier-in-the-Loop Target Acquisition Performance Prediction Through 2001: Integration of Perceptual and Cognitive Models

Vaughan¹

2006

PsycEXTRA Dataset

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, ARL-TR-3833 SPONSOR/MONITOR'S ACRONYM(S) 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release; distribution is unlimited. SUPPLEMENTARY NOTES ABSTRACTModeling Soldier-in-the-loop target acquisition performance is necessary for the development of improved sensors, more effective training methods, and better war game simulations. Accurately modeling this performance requires a detailed understanding of the environment, how a sensor responds to the environment, how it displays information to an observer, and how the observer employs that information to acquire a target. The first three requirements have been met; the fourth requirement, however, has not yet been achieved. Attempts to model the observer's visual and decision-making processes have been compromised by the analysis of the scene, based on physical parameters alone rather than how the visual system interprets the scene. Models based on such scene-derived factors have had limited success.This report takes a two-pronged approach to how future models can be improved by the sensible integration of human visual processing. One prong concerns basic research from the perceptual psychology community. Over the last few decades, this research has generated a detailed theoretical understanding of visual processing and decision making, based on visual information. The other prong concerns important models, modeling frameworks, and scene metrics from the military target acquisition community. Particular attention is paid to issues of clutter, the extendibility of the Johnson criteria, classical and neoclassical search frameworks, the selection of methods and performance metrics, and existing Night Vision and Electronic Sensors Directorate models. Issues related to the validation of target acquisition models are also discussed. iii Abstract (continued)Existing target acquisition models tend to base performance on (a) one-dimensional (1-D) metrics defining the amount of information in the target (e.g., resolvable bar cycles, contrast, area, size, perimeter, speed of motion) and how that information correlates to level of performance in a target acquisition task (i.e., detection, classification, recognition, and identification), (b) search processes that are unrealistic (e.g., that assume random eye movements), and (c) 1-D metrics to define the whole scene (clutter) or regions of the scene (e.g., clutter, conspicuity, attractiveness). These tendencies fail to account for known human behavior, although models incorporating them may be insensitive t...

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“…Finally, TOD can also be used as the basis for range predictions, and in limited trials, has actually produced better correlation to target-acquisition range than bar-target testing. 8,9 An important distinction between MRT and TOD is the duration of the observers' task. When measuring MRT, the target contrast is gradually adjusted as the observer stares at the imagery.…”

Section: Todmentioning

confidence: 99%

TOD versus MRT when evaluating thermal imagers that exhibit dynamic performance

et al. 2003

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While it is universally recognized that image quality of a thermal sensor is a strong function of spatial uniformity, the metrics commonly used to assess performance do not adequately measure the effectiveness of non-uniformity correction (NUC). Image uniformity is generally not static, particularly if correction terms are updated intermittently (with periodic shuttering) or gradually (with scene-based NUC). Minimum Resolvable Temperature (MRT), the most prevalent test for characterizing overall imaging performance, is poorly suited for characterizing dynamic performance. The Triangle Orientation Discrimination (TOD) metric proposed by Bijl and Valeton, because of its short observation window, provides better capability for evaluating sensors that exhibit non-negligible uniformity drift. This paper compares the effectiveness of MRT and TOD for measuring dynamic performance. TOD measurements of a shutter-based thermal imager are provided immediately after shutter correction and 3 minutes later. The drift in TOD performance shows excellent corre lation to drift in system noise. BACKGROUNDJust five years ago, achieving noise equivalent temperature difference (NEdT) of 100 mK with an uncooled detector was considered a daunting challenge; today, several uncooled cameras tout sensitivity below 25 mK . Furthermore, pixel sizes have been continuously shrinking while focal plane array (FPA) formats have grown larger. As a result of the rapid progress, the uncooled infrared community places considerable attention on these three attributes -temporal NEdT, pixel size, and array format. Indeed, these parameters have emerged as de facto criteria by which uncooled systems are compared and judged. Unfortunately, these criteria completely neglect spatial uniformity (also called spatial noise) as a component of image quality. Consequently, the importance of effective NUC is often disregarded despite experimental evidence that spatial noise is in fact more detrimental to overall performance than temporal. 1 A more complete method of evaluating a Forward Looking Infrared (FLIR) system is MRT, which is commonly held as the best measure of overall imaging performance. One of the positive attributes of MRT is that it includes spatial noise in the assessment of performance. However, this advantage is undermined by the fact that no standard methods have been defined for adequately representing true NUC effectiveness when measuring MRT. For example, there are no definitive guidelines prescribing how often to update NUC terms when evaluating a system that periodically employs a shutter-based correction or how to handle performance drift between updates. Furthermore, MRT test conditions are poorly suited for assessing systems that use scenebased NUC, and there are no defined procedures for resolving this issue either. So while MRT does not completely disregard spatial noise, it does not always capture true performance in real-world conditions. Therefore, results can be very deceiving.Significant resources are being directed by the ...

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Validation of the new triangle orientation discrimination method and ACQUIRE model predictions using observer performance data for ship targets

Cited by 43 publications

References 5 publications

TOD, NVTherm, and TRM3 model calculations: a comparison

TOD, NVTherm, and TRM3 model calculations: a comparison

Soldier-in-the-Loop Target Acquisition Performance Prediction Through 2001: Integration of Perceptual and Cognitive Models

TOD versus MRT when evaluating thermal imagers that exhibit dynamic performance

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