This research investigated the effects of using medical imaging enhancement techniques to increase the detectability of targets in the urban terrain. Targets in the urban environment present human observers different challenges than targets located in the traditional, open field, search environment. In the traditional environment, targets typically were military vehicles in a natural background. In the urban environment, targets were humans against a man-made background. The U.S. Army Night Vision and Electronic Sensors Directorate (NVESD) and the U. S. Army Research Laboratory (ARL) explored three image processing techniques: contrast enhancement, edge enhancement, and a multiscale edge domain process referred to as "mountain-view". For the mountain-view presentation, high-contrast edges were enhanced. Human perception experiments were conducted with non-enhanced real imagery collected from an Urban Operations training center. These human perception experiments establish a baseline response. Processing the imagery using the previously mentioned techniques then allowed human perception experiments to be conducted. The performance parameters used for comparison were probability of detection, and time required to detect a target. This research provided a methodology of evaluating and quantifying human performance differences in target acquisition based on image processing techniques in the urban environment.
When modeling the search and target acquisition process, probability of detection as a function of time is important to war games and physical entity simulations. Recent US Army RDECOM CERDEC Night Vision and Electronics Sensor Directorate modeling of search and detection has focused on time-limited search. Developing the relationship between detection probability and time of search as a differential equation is explored. One of the parameters in the current formula for probability of detection in time-limited search corresponds to the mean time to detect in time-unlimited search. However, the mean time to detect in time-limited search is shorter than the mean time to detect in time-unlimited search and the relationship between them is a mathematical relationship between these two mean times. This simple relationship is derived.
In the urban operations (UO) environment, it may be necessary to identify various vehicles that can be referred to as non-traditional vehicles. A police vehicle might require a different response than a civilian vehicle, or a tactical vehicle. This research reports the measured 50% probability of identification cycle criteria (N50s and V50s) required to identify a different vehicle set than previously researched at NVESD. Longwave infrared (LWIR) and midwave infrared (MWIR) imagery of twelve vehicles at twelve different aspects was collected. Some of the vehicles in this confusion set include an ambulance, a police sedan, a HMMWV, and a pickup truck. This set of vehicles represents those commonly found in urban environments. The images were blurred to reduce the number of resolvable cycles. The results of the human perception experiments allowed the 50% probability of identification cycle criteria (N50s and V50s) to be measured. These results will allow the modeling of sensor performance in the urban terrain for infrared imagers.
In the urban environment, it may be necessary to identify personnel based on their type of dress. Observing a police officer or soldier might require a different response than observing an armed civilian. This paper reports on the required number of resolvable cycles to identify different personnel based upon the variations of their clothing and armament. Longwave (LWIR), and midwave infrared (MWIR) images of twelve people at twelve aspects were collected. These images were blurred and 11 human observers performed a 12-alternative forced choice visual identification experiment. The results of the human perception experiments were used to measure the required number of resolvable cycles for identifying these personnel. These results are used in modeling sensor performance tasks and improving war-game simulations oriented to the urban environment.
This research compares target detection in the longwave and midwave spectral bands in urban environments. The Night Vision and Electronic Sensors Directorate (NVESD) imaged one hundred scenes at several Army Military Operations in the Urban Terrain (MOUT) sites during day and night. Images were resized to make the field-of-view (FOV) for each scene approximately the same. These images were then presented in a time-limited search perception experiment using military observers. Probabilities of detection were compared between the two spectral bands. Results from MOUT search were compared with previous modeling efforts.
Recent technology advances have made low cost, eye safe, high performance laser-range-gated (LRG) imagers a reality. These advances include the Electron Bombarded CCD sensor and the 1.5 micron, monoblock laser. LRG imagers use a laser beam to illuminate targets at extended ranges; the targets are then identified with the EBCCD sensor. Several features of LRG imagers make predicting range performance different than for passive imagers. LRG imagers are described. The features that make active imager performance different from passive imager performance are discussed. Features unique to active imagers include laser speckle in the image, the narrow illumination beam and its interaction with the atmosphere, the highly directional “spot light” illumination of the target, and the range gating of the receiver. This paper discusses the unique modeling requirements for LRG imagers.
The U.S. Army and the U.S. Air Force are investigating laser range-gated shortwave infrared (LRG-SWIR) imaging systems for use in target identification. When coupled to an electron-bombarded CCD, the imaging system can obtain high- resolution images at long ranges. Speckle, an image artifact inherent in laser illuminated imaging systems, results from interference patterns caused by the coherent illumination. Laser speckle degrades target identification performance but can be reduced by averaging successive LRG-SWIR images. This research is a first attempt at quantifying target identification performance degradation associated with laser speckle. The research begins with a laboratory experiment to verify a speckle model that includes power spectral density and intensity probability density functions. An LRG-SWIR sensor simulation is developed that includes coherent illumination resulting in speckle target images. A field demonstration is performed to verify the fidelity of the simulation. The simulation is then applied to the NVESD target identification set with various levels of image averaging and blur. Observer performance results are analyzed in terms of target identification probability and the effects of various levels of blur and speckle are characterized.
Display artifacts such as raster and square pixelization associated with flat panel displays can be characterized using sampled imaging systems analysis. When the output raster/pixelization signal is large compared to the image modulation, the overall system performance is degraded. In this research, we investigated display methods for sampled imaging systems, including pixel replication, bilinear interpolation, and a higher-order interpolation. Problems with the performance modeling of these processes are discussed and a perception test is implemented for comparison.
The effect of sampling, or aliasing, on target recognition performance (discriminating between armored tracked, armored wheeled, and soft wheeled classes of tactical vehicles) is investigated in this research. A recognition target set was processed with various levels of blur and aliasing. Integrated spurious response levels were set to 0, 0.3, 0.6, and 0.9. A perception experiment was conducted with U.S. Army soldiers at Ft. Benning, Georgia to determine the impact of blur and aliasing on recognition performance. The results are described in this paper.
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