Theodore J. Doll

Observer false alarm effects on detection in clutter

Previous sensor/observer performance prediction models have not explicitly treated false alarm effects, especially in relation to clutter. A method for predicting probabilities of detection as a function of observer false alarm probability and clutter is presented. The method is applied to previously collected observer data to determine the effect of observer false alarm probability on resolution criteria associated with specified clutter levels. The interdependent effects of clutter and observer false alarm probability are illustrated by deriving an expression for the probability of target acquisition in the case where the observer has only enough time to select one target candidate. It is concluded that false alarm probability has a significant impact on target resolution criteria in moderate and high background clutter. Example resolution criteria are given for several false alarm probabilities.

Auditory signals in military aircraft: ergonomics principles versus practice.

The complete ensembles of auditory signals in selected USAF aircraft (the F-4D, F-15, two models of the F-16, the C-5, and the C-141) are described and evaluated. Human factors research related to the design of speech and non-speech auditory signals is reviewed. Major findings are: that auditory signals are not well standardised among the aircraft, even between those with similar combat roles; that a relatively large number of non-speech auditory signals are used, which may make it difficult for the aircrew to recall the meanings of all the signals; that some non-speech signals are sufficiently similar that they may be confused, particularly in high workload and stressful conditions; and that the criticality of the warnings is not reliably indicated by any characteristic of the signals. Four problem areas requiring further research are discussed: reduction of signal loudness, enhancement of the distinctiveness and masking resistance of non-speech signals, effects of concurrent warning signals on aircrew performance, and additional uses of auditory information.

Robust, sensor-independent target detection and recognition based on computational models of human vision

Most current artificial vision systems lack robustness and are applicable only to a narrow range of tasks. Crevier (1997) has suggested that this is due to their reliance on a small number of vision mechanisms and to lack of knowledge about how vision algorithms should be integrated. We suggest a systems approach to artificial vision based on computational vision research. The capabilities of biological vision systems are contrasted with those of current piecemeal approaches to artificial vision. A mature, comprehensive vision system, called the Georgia Tech Vision (GTV) simulation is described. GTV incorporates quasilinear filter mechanisms to simulate the processing performed by simple and complex cortical cells. The outputs of these mechanisms are adaptively combined to discriminate targets from clutter and/or one another. GTV outputs predictions of human search and detection performance and/or targeting metrics for automatic target recognition (ATR) applications. Studies validating GTV as a model of human search and detection performance and demonstrating its performance as an ATR are presented.

Target and background characterization based on a simulation of human pattern perception

Work in progress at Georgia Tech to develop a model of human pattern perception, visual search, and detection is reviewed. The models algorithms are based on research on low-level visual processes. Recent advances in the field have led to the development of computational models of the image processing performed by the visual system from the cornea to the striate cortex. The model also incorporates recent advances from research on visual search. The organization of the model for predicting target acquisition, analyzing target signatures, and specifying low-observable requirements is discussed.

Synthesis of Auditory Localization Cues for Cockpit Applications

The long-term objective of this work is to develop techniques for conveying accurate spatial information via audio signals delivered to the listener through headphones. Specific objectives of the first phase included the design, fabrication, and evaluation of an apparatus for demonstrating simulated auditory localization (SAL). The design of the SAL facility is described. An experimental test of the psychological fidelity of the SAL facility is summarized. The results show that the facility produces a high-fidelity simulation of normal, unaided auditory localization.

Simulation of Human Visual Search in Cluttered Backgrounds

Work in progress at Georgia Tech to develop a model of human pattern perception, visual search, and detection is reviewed. The models algorithms are based on research on low-level visual processes. Recent advances in that field have led to the development of computational models of the image processing performed by the visual system from the cornea to the striate cortex. The model also incorporates recent advances from research on visual search. The organization of the model is described, and the results of some preliminary tests are presented.

Guidelines for developing and validating models of visual search and target acquisition

Some shortcomings of past and current approaches for modeling human visual search and target acquisition (STA) are discussed. The effects of complex pattern perception, visual attention, learning, and cognition on STA performance are particularly emphasized. The importance of these processes is explained and approaches are suggested for modeling them. Guidelines are also provided for testing and validating models of visual STA. These guidelines take into account the roles of pattern perception, visual attention, learning, and cognition in STA performance. We also present and compare alternative approaches to field testing for the purpose of model validation.

Biologically based vision simulation for target-background discrimination and camouflage/LO design

The Georgia Tech Research Institute has developed an integrated suite of software for Visual and Electro-Optical (VISEO) detection analysis, under the sponsorship of the Army Aviation and Troop Command, Aviation Applied Technology Directorate. The VISEO system is a comprehensive workstation-based tool for multi-spectral signature analysis, LO design, and visualization of targets moving through real measured backgrounds. A key component of the VISEO system is a simulation of real measured backgrounds. A key component of the VISEO system is a simulation of human vision, called the Georgia Tech Vision (GTV) simulation. The algorithms used in the simulation are consistent with neurophysiological evidence concerning the functions of the human visual system, from dynamic light adaptation processes in the retinal receptors and ganglia to the processing of motion, color, and edge information in the striate cortex. The simulation accepts images seen by the naked eye or through direct-view optical systems, as well as images viewed on the displays of IR sensors, image intensifiers and night-vision devices. GTV outputs predicted probabilities that the target is fixated (Pfix) during visual search, and detected (Pd), and also identifies specific features of the target that contribute most to successful search and detection performance. This paper outlines the capabilities and structure of the VISEO system, emphasizing GTV. Example results of visible and IR signature reduction on the basis of VISEO will be shown and described.

Biologically inspired image interpretation and automatic target recognition technologies

Biologically-based computer vision systems are now available that achieve robust image interpretation and automatic target recognition (ATR) performance. We describe two such systems and the reasons behind their robust performance. We also report results of three studies that demonstrate this robustness.

Visual Search and Detection in Background Clutter

Two traditions of vision modeling have coexisted for many years with little or no transfer of information between them. Those interested in models of visual target acquisition for real-world scenarios have developed engineering models, which are essentially empirical summaries of visual performance data. On the other hand, basic researchers in visual psychophysics and neurophysiology have developed quantitative models of pattern perception. The basic research models have increased in generality and scope to the point that they are potentially powerful tools for addressing certain real-world needs that have recently come to the fore. The needs include quantitative, theory-based methods for evaluating target signatures, effects of background clutter, and observer false alarm rates. This paper reviews the shortcomings of existing target acquisition models, and reports work in progress to develop an improved model of target acquisition that incorporates a model of pattern perception from basic vision research.