The objective of this research was to determine if there was an improvement in human observer performance, identifying potential weapons or threat objects, when imagery is presented in three dimensions instead of two dimensions. Also it was desired to quantify this potential improvement in performance by evaluating the change in N50 cycle criteria, for this task and target set. The advent of affordable, practical and real-time 3-D displays has led to a desire to evaluate and quantify the performance trade space for this potential application of the technology.
The imagery was collected using a dual camera stereo imaging system. A series of eight different resolutions were presented to observers in both two and three dimensional formats. The set of targets consisted of twelve hand held objects. The objects were a mix of potential threats or weapons and possible confusers. Two such objects, for example, are a cellular telephone and a hand grenade. This target set was the same target set used in previously reported research which determined the N50 requirements for handheld objects for both visible and infrared imagers.
Different systems are optimized for and are capable of addressing issues in the different spectral regions. Each sensor has its own advantages and disadvantages. The research presented in this paper focuses on the fusion of MWIR (0.3-0.5 μm) and LWIR (0.8-12 μm) spectrums on one IR Focal Plane Array (FPA). The information is processed and then displayed in a single image in an effort to analyze possible benefits of combining the two bands. The analysis addresses how the two bands differ by revealing the dominant band in terms of temperature value for different objects in a given scene, specifically the urban environment
Three perception experiments were conducted to quantify the relationship between tactical military vehicle identification (ID) performance when using an imager and the Modulation Transfer Function and noise characteristics of that imager. The results of these experiments show that the limiting resolution metric provides a reasonably accurate prediction of target ID performance. For example, limiting resolution is a better predictor of performance than Modulation Transfer Function Area or Integrated Contrast Sensitivity. However, a metric consisting of integrating the square root of the product of Contrast Sensitivity and spatial frequency provides a better fit to data than limiting resolution. This paper describes the perception experiments and test results. The predictive capability of a selected group of image quality metrics is evaluated. This paper also discusses possible improvements to target acquisition performance models.