Night vision integrated performance model: impact of a recent change on the model's predictive accuracy.

The U. S. Army has adopted the targeting task performance (TTP) image quality metric for formal analyses of electro-optical imager performance. The most recent release of the model, NVIPM, changes the way TTP is calculated in order to improve the accuracy of facial identification. Predictions of the original and modified models are compared to both laboratory and field data. The change degrades NVIPM predictive accuracy for the tactical vehicle target set which is often used in hardware procurement specifications.

Degradation of near infrared and shortwave infrared imager performance due to atmospheric scattering of diffuse night illumination.

On moonless nights, airglow is the primary source of natural ground illumination in the near infrared and shortwave infrared spectral bands. Therefore, night vision imagers operating in these spectral bands view targets that are diffusely illuminated. Aerosol scattering of diffuse airglow illumination causes atmospheric path radiance and that radiance causes increased imager noise. These phenomena and their quantification are described in this paper.

Small obstacle avoidance sensor.

This paper describes a laser ranging sensor that is suitable for applications like small unmanned aerial vehicles. The hardware consists of a diode emitter array and line-scan charge coupled devices. A structured-light technique measures ranges up to 30 meters for 64 field angles in a 90 degree field of view. Operation is eye safe, and the laser wavelength is not visible to night vision goggles. This paper describes a specific sensor design in order to illustrate performance for a given package size.

Design of finite impulse response deconvolution filters.

This paper describes inverse filters that restore imagery with minimal artifacts. Deconvolution kernels are obtained by windowing Wiener-type inverse filters (WTIFs). Windowed WTIFs exhibit good restoration properties. Constraining the size of the WTIF kernel is necessary because of the edge effects associated with discrete convolutions. Spatial windowing is necessary even if processing is performed in the frequency domain. The kernel design procedure is described, and the theory is explained. The performance penalty associated with limiting kernel size is discussed and quantified.

Representing the observer in electro-optical target acquisition models.

Electro-optical target acquisition models predict the probability that a human observer recognizes or identifies a target. To accurately model targeting performance, the impact of imager blur and noise on human vision must be quantified. In the most widely used target acquisition models, human vision is treated as a "black box" that is characterized by its signal transfer response and detection thresholds. This paper describes an engineering model of observer vision. Characteristics of the observer model are compared to psychophysical data. This paper also describes how to integrate the observer model into both reflected light and thermal sensor models.

Modeling the target acquisition performance of active imaging systems.

Recent development of active imaging system technology in the defense and security community have driven the need for a theoretical understanding of its operation and performance in military applications such as target acquisition. In this paper, the modeling of active imaging systems, developed at the U.S. Army RDECOM CERDEC Night Vision & Electronic Sensors Directorate, is presented with particular emphasis on the impact of coherent effects such as speckle and atmospheric scintillation. Experimental results from human perception tests are in good agreement with the model results, validating the modeling of coherent effects as additional noise sources. Example trade studies on the design of a conceptual active imaging system to mitigate deleterious coherent effects are shown.

Predicting range performance of sampled imagers by treating aliased signal as target-dependent noise.

This paper presents a new theory to predict the impact of sampling on target acquisition. The aliased signal that results from sampling is treated as noise. The aliased signal is different from detector noise in two ways. First, aliasing disappears as the target contrast decreases. Second, the image corruption due to aliasing gets worse with increased range. This is because sampling is constant in angle space, and targets become poorly sampled as range increases. The theory is presented, along with the results of three experiments. The match between model and experiment is excellent.

Predicting the probability of facial identification using a specific object model.

Many security and surveillance tasks involve either finding an object in a cluttered scene or discriminating between like objects. For example, an observer might look for a person of known height and weight in a crowd, or he might want to positively identify a specific face. The paper "Modeling target acquisition tasks associated with security and surveillance" [Appl. Opt. 46, 4209 (2007)] describes a specific-object model used to predict the probability of accomplishing this type of task. We describe four facial identification experiments and apply the specific-object model to predict the results. Facial identification is accurately predicted by the specific-object model.

Modeling target acquisition tasks associated with security and surveillance.

Military sensor applications include tasks such as the surveillance of activity and searching for roadside explosives. These tasks involve identifying and tracking specific objects in a cluttered scene. Unfortunately, the probability of accomplishing these tasks is not predicted by the traditional detect, recognize, and identify (DRI) target acquisition models. The reason why many security and surveillance tasks are functionally different from the traditional DRI tasks is described. Experiments using characters and simple shapes illustrate the problem with using the DRI model to predict the probability of identifying individual objects. The current DRI model is extended to predict specific object identification by including the frequency spectrum content of target contrast. The predictions of the new model match experimental data.

Atmospheric modulation transfer function in the infrared.

In high-resolution ultranarrow field-of-view thermal imagers, image quality over relatively long path lengths is typically limited by atmospheric degradation, especially atmospheric blur. We report our results and analyses of infrared images from two sites, Fort A. P. Hill and Aberdeen Proving Ground. The images are influenced by the various atmospheric phenomena: scattering, absorption, and turbulence. A series of experiments with high-resolution equipment in both the 3-5- and 8-13-microm regions at the two locations indicate that, as in the visible, image quality is limited much more by atmosphere than by the instrumentation for ranges even of the order of only a few kilometers. For paths close to the ground, turbulence is more dominant, whereas for paths involving higher average elevation, aerosol modulation transfer function (MTF) is dominant. As wavelength increases, turbulence MTF also increases, thus permitting aerosol MTF to become more dominant. A critical role in aerosol MTF in the thermal infrared is attributed to absorption, which noticeably decreases atmospheric transmission much more than in the visible, thereby reducing high-spatial-frequency aerosol MTF. These measurements indicate that atmospheric MTF should be a basic component in imaging system design and analysis even in the infrared, especially as higher-resolution hardware becomes available.