&lt;title&gt;Range performance of two staring imagers: presentation of the field trial and data analysis&lt;/title&gt;

Currently, three major approaches exist to predict Target Acquisition (TA) performance with thermal imagers: i) the TOD laboratory method and model, ii) the NVThermIP model and iii) the MTDP lab method and TRM3 model. In this study, TOD measurements, TOD predictions and NVThermIP predictions are compared for a number of simulated sensors ranging from very well-sampled to highly under-sampled. A similar comparison study using a previous (2001) version of the NVTherm model showed huge differences in sensor performance predictions SPIE Proceedings Vol. 4719, 51-62;2002). The most important result of the current study is that NVThermIP predictions are much closer to the TOD measurements and predictions than those of its predecessor, showing limited effect of under-sampling. Quantitatively, TA range predictions for well-sampled imagers are equivalent and NVThermIP predicts 25% longer ranges than the TOD model for under-sampled imagers with M P = 0.35· V P and β = 1.25, where V P are the criteria published with NVThermIP to predict TA range for a variety of target sets, M P are the corresponding TOD magnification factors, and β is the slope of the probability vs range function in the TOD target acquisition model. Which method yields the best predictions under which circumstances should be the subject of an empirical study using TA performance for real targets. It is therefore advised that all available TA validation data be presented in such a way that all models and methods can be compared to the data directly and unambiguously.

Section: Introductionmentioning

confidence: 97%

NVThermIP vs TOD: matching the target acquisition range criteria

Bijl¹,

Hogervorst²

2007

“…For decades, the focus of Target Acquisition performance measurement and modeling has been on static imaging of military vehicles at long range using a high-end scanning or staring thermal imager 1,2,3,4,5,6 . In recent years an important shift of interest has taken place to other scenes with different objects of interest and different types of sensors.…”

Section: Introductionmentioning

confidence: 99%

Target acquisition performance: effects of target aspect angle, dynamic imaging, and signal processing

Beintema¹,

Bijl²,

Hogervorst³

et al. 2008

In an extensive Target Acquisition (TA) performance study, we recorded static and dynamic imagery of a set of military and civilian two-handheld objects at a range of distances and aspect angles with an under-sampled uncooled thermal imager. Next, we applied signal processing techniques including DSR (Dynamic Super Resolution) and LACE (Local Adaptive Contrast Enhancement) to the imagery. In a perception experiment, we determined identification (ID) and threat/non-threat discrimination performance as a function of target range for a variety of conditions. The experiment was performed to validate and extend current TA models. In addition, range predictions were performed with two TA models: the TOD model and NVThermIP. The results of the study are: i) target orientation has a strong effect on performance, ii) the effect of target orientation is well predicted by the two TA models, iii) absolute identification range is close the range predicted with the two models using the recommended criteria for two-handheld objects, iv) there was no positive effect of sensor motion on performance, and this was against the expectations based on earlier studies, v) the benefit of DSR was smaller than expected on the basis of the model predictions, and vi) performance with LACE was similar to performance on an image optimized manually, indicating that LACE can be used to optimize the contrast automatically. The relatively poor results with motion and DSR are probably due to motion smear induced by a higher camera speed than used in earlier studies. Camera motion magnitude and smear are not yet implemented in TA models.

“…The two most promising alternative approaches to the TOD are the TTP (Targeting Task Performance) metric 35,36,37 and the MTDP test method in combination with the TRM3 range performance model 38,39 .…”

Section: Correspondence With Other Approachesmentioning

confidence: 99%

“…The Minimum Temperature Difference Perceived (MTDP) model 38,39 is an adaptation of the MRTD to make it applicable to undersampled imagers. TRM3 is the TA range model associated with the MTDP, and the model is available as a mature software package.…”

Section: Correspondence With Other Approachesmentioning

confidence: 99%

Visual acuity and contrast sensitivity with compressed motion video

Bijl¹,

Vries²

2009

Video of Visual Acuity (VA) and Contrast Sensitivity (CS) test charts in a complex background was recorded using a CCD camera mounted on a computer-controlled tripod and fed into real-time MPEG2 compression/decompression equipment. The test charts were based on the Triangle Orientation Discrimination (TOD) test method and contained triangle test patterns of different sizes and contrasts in four possible orientations. In a perception experiment, VA and CS thresholds at the 75% correct level were obtained for three camera velocities (0, 1.0 and 2.0 deg/s or 0, 4.1 and 8.1 pix/frame) and four compression rates (no compression, 4Mb/s, 2Mb/s and 1 Mb/s). VA is shown to be rather robust to any combination of motion and compression. CS however dramatically decreases when motion is combined with high compression ratios. The data suggest that with the MPEG2 algorithm the emphasis is on the preservation of image detail at the cost of contrast loss.