Theory of signal detection and its application to visual target acquisition: A review of the literature.

1992 ◽  
Author(s):  
Denise L. Wilson
Keyword(s):  
Signal Detection ◽  
Visual Target ◽  
Target Acquisition ◽  
Review Of The Literature

A Model of Air to Ground Target Acquisition Based on Logistic Regression Analysis

2000 ◽  
Vol 44 (21) ◽  
pp. 3-484-3-487
Author(s):  
Bartholomew Elias
Keyword(s):  
Logistic Regression ◽  
Signal Detection ◽  
Human Performance ◽  
Visual Target ◽  
Target Acquisition ◽  
Independent Variables ◽  
Ground Target ◽  
Regression Techniques ◽  
Detection Capabilities ◽  
Operational Constraints

Logistic regression, a technique for describing relationships between a binary or dichotomous dependent variable and one or more independent variables that can be either discrete or continuous, is demonstrated to be an effective analytical tool for evaluating data collected using psychophysical methods and signal detection procedures. One specific application of logistic regression is the assessment of operational factors on human performance in visual target acquisition. Visual target acquisition data collected using signal detection procedures were reanalyzed using logistic regression techniques. The application of these logistic regression techniques produced empirically derived psychophysical models of target detection capabilities under various conditions. Such models can be used to predict human performance in visual target acquisition under various operational constraints.

Spatial Multisensory Cueing to Support Visual Target-Acquisition Performance

2012 ◽  
Vol 56 (1) ◽  
pp. 1312-1316 ◽  
Author(s):  
Julio C. Mateo ◽  
Brian D. Simpson ◽  
Robert H. Gilkey ◽  
Nandini Iyer ◽  
Douglas S. Brungart
Keyword(s):  
Visual Target ◽  
Target Acquisition ◽  
Acquisition Performance

Visual Target Acquisition: Bridging the Gap from Vision Research to Applied Models

1992 ◽  
Vol 36 (18) ◽  
pp. 1418-1419
Author(s):  
Theodore J. Doll
Keyword(s):  
Vision Research ◽  
Visual Target ◽  
Lessons Learned ◽  
The Other ◽  
Bad News ◽  
Good News ◽  
Target Acquisition ◽  
Ground Vehicles ◽  
Desert Storm ◽  
Highly Effective

One of the lessons learned in Desert Storm is that visual and electro-optical (VISEO) systems are highly effective. Most of us recall seeing CNN footage of EO sensors acquiring targets and guiding weapons to destroy the target. The effectiveness of VISEO systems is the good news. The bad news is that the other side is likely to be equipped with similar VISEO systems in the next war. Our personnel and materiel are therefore likely to be highly vulnerable to such systems in future conflicts. There is an urgent need to make our weapons systems, especially ground vehicles, less conspicuous on the battlefield, i.e., to develop more effective camouflage and signature suppression techniques.

Controlling Contrast in Target Acquisition Simulations Involving Complex Backgrounds

1992 ◽  
Vol 36 (18) ◽  
pp. 1435-1439
Author(s):  
William Kosnik
Keyword(s):  
Simple Technique ◽  
Visual Target ◽  
Systematic Effect ◽  
Target Acquisition ◽  
Low Contrast ◽  
Performance Factors ◽  
Background Structure ◽  
Inherent Feature ◽  
Uniform Background ◽  
Natural Terrain

Visual target acquisition (TA) often involves detecting targets against natural backgrounds that have complex luminance distributions. The purpose of this study was to evaluate a simple technique that controls target contrast in the presence of varying backgrounds. Target contrast was measured by the root mean square (rms) method and was controlled by adjusting only the target luminance, leaving the background unchanged. The technique was tested in a TA paradigm in which observers searched for an aircraft that was embedded in 1) a uniform background, 2) a natural terrain background, or 3) a moving natural terrain background. Four target contrast levels were tested. The results showed that TA time varied with background and target contrast. Significant differences in TA time were observed among the different backgrounds for targets of the same physical contrast, especially at low contrast levels. Although contrast had a systematic effect on TA performance, factors other than contrast influenced TA performance. It was concluded that background structure increased TA time by camouflaging targets and by introducing distractors to the task. Such an approach could be used to model TA performance under conditions where target and background complexity are an inherent feature of the TA task.

Visual Search and Detection in Background Clutter

1992 ◽  
Vol 36 (18) ◽  
pp. 1420-1424
Author(s):  
Theodore J. Doll ◽  
Shane W. McWhorter ◽  
David E. Schmieder
Keyword(s):  
Real World ◽  
Vision Research ◽  
Visual Target ◽  
Basic Research ◽  
Pattern Perception ◽  
Target Acquisition ◽  
Engineering Models ◽  
Background Clutter ◽  
Improved Model ◽  
Transfer Of Information

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.

Combined 3-D auditory-visual cueing for a visual target acquisition task

2007 ◽  
Author(s):  
Rachael L. Westergren ◽  
Paul R. Havig ◽  
Eric L. Heft
Keyword(s):  
Visual Target ◽  
Target Acquisition ◽  
Visual Cueing ◽  
Acquisition Task

Spatial Multisensory Cueing to Support Visual Target-Acquisition Performance

2012 ◽  
Author(s):  
Julio C. Mateo ◽  
Brian D. Simpson ◽  
Robert H. Gilkey ◽  
Nandini Iyer ◽  
Douglas S. Brungart
Keyword(s):  
Visual Target ◽  
Target Acquisition ◽  
Acquisition Performance

scholarly journals Individual vocal recognition across taxa: a review of the literature and a look into the future

2020 ◽  
Vol 375 (1802) ◽  
pp. 20190479 ◽  
Author(s):  
Nora V. Carlson ◽  
E. McKenna Kelly ◽  
Iain Couzin
Keyword(s):  
Signal Detection ◽  
Signal Detection Theory ◽  
Experimental Tests ◽  
Mate Recognition ◽  
Review Of The Literature ◽  
Theme Issue ◽  
Animal Kingdom ◽  
Vocal Recognition ◽  
Recognition Systems ◽  
Evolving Models

Individual vocal recognition (IVR) has been well studied in mammals and birds. These studies have primarily delved into understanding IVR in specific limited contexts (e.g. parent–offspring and mate recognition) where individuals discriminate one individual from all others. However, little research has examined IVR in more socially demanding circumstances, such as when an individual discriminates all individuals in their social or familial group apart. In this review, we describe what IVR is and suggest splitting studies of IVR into two general types based on what questions they answer (IVR-singular, and IVR-multiple). We explain how we currently test for IVR, and many of the benefits and drawbacks of different methods. We address why IVR is so prevalent in the animal kingdom, and the circumstances in which it is often found. Finally, we explain current weaknesses in IVR research including temporality, specificity, and taxonomic bias, and testing paradigms, and provide some solutions to address these weaknesses. This article is part of the theme issue ‘Signal detection theory in recognition systems: from evolving models to experimental tests’.

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