Target Acquisition in Cluttered Environments

Birkmire, Deborah P.; Karsh, Robert; Barnette, B. Diane; Pillalamarri, Ramakrishna

doi:10.1177/154193129203601814

Cited by 9 publications

(10 citation statements)

References 3 publications

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“…We can see that the SV metric is proposed on the assumption that the visual system is sensitive to the areas with high gray-level variability. Many experiments have showed that SV is suitable for most natural scenes, but could not quantify the urban environment accurately [13], and is poor in predicting overall search time [14]. Silk proposed an improved metric (SSV) [15] of SV by fully considering the difference between every pixel and its neighborhood region through the whole clutter image.…”

Section: Introductionmentioning

confidence: 99%

DSIM: A DisSIMilarity-Based Image Clutter Metric for Targeting Performance

Shi

2013

IEEE Trans. on Image Process.

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Previous image clutter metrics were proposed on the thought that clutter was just a perceptual effect, while we identify clutter as both perceptual and cognitive effects. Under this identification, we give a new definition of image clutter metric by analyzing the research results in the fields of visual psychology and psychophysics. According to the definition, we further put forward a DisSIMilarity (DSIM) based image clutter metric, which can also be taken as a kind of HVS-based signal-to-clutter ratio. The earlier image clutter metrics produced limited success in predicting targeting performance mainly since they did not consider brain cognitive characteristics. We develop a brain cognitive dissimilarity measure (BCDM) as a quantitative estimate of the selection weights which are allocated by brain attentional mechanism to affect visual selection processes. A human vision perceptual dissimilarity measure (VPDM), fully embodying vision perceptual properties, is first established between the target and clutter images, and then we utilize the BCDM between the two images as selection weights to pool the VPDM to be a clutter metric, which can be called DSIM metric. The metric is tested in Search_2 dataset provided by TNO Human Factors Research Institute of Netherlands. Error analysis and correlation tests demonstrate that the DSIM metric makes a more significant improvement than previously proposed metrics in predicting 62 observers' targeting performances including detection probability, false alarm probability and search time.

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Section: Introductionmentioning

confidence: 99%

DSIM: A DisSIMilarity-Based Image Clutter Metric for Targeting Performance

Shi

2013

IEEE Trans. on Image Process.

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“…These oscillations have been seen at short times in other experiments, notably a 1993 experiment conducted at the Human Engineering Laboratory, Aberdeen, MD [Birkmire et al (1992)]. They have been simulated numerically in a Monte Carlo simulation by J.F.…”

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confidence: 99%

A Search for Understanding. Analysis of Human Performance on Target Acquisition and Search Tasks using Eyetracker Data.

Nicoll¹,

Hsu²

1995

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IDA Log No. HQ 94-46051 DEFINITIONSIDA publishes the following documents to report the results of its work. ReportsReports are the most authoritative and most carefully considered products IDA publishes. They normally embody results of major projects which (a) have a direct bearing on decisions affecting major programs, (b) address issues of significant concern to the Executive Branch, the Congress and/or the public, or (c) address issues that have significant economic implications. IDA Reports are reviewed by outside panels of experts to ensure their high quality and relevance to the problems studied, and they are released by the President of IDA. Group ReportsGroup Reports record the findings and results of IDA established working groups and panels composed of senior individuals addressing major issues which otherwise would be the subject of an IDA Report. IDA Group Reports are reviewed by the senior individuals responsible for the project and others as selected by IDA to ensure their high quality and relevance to the problems studied, and are released by the President of IDA. PapersPapers, also authoritative and carefully considered products of IDA, address studies that are narrower in scope than those covered in Reports. IDA Papers are reviewed to ensure that they meet the high standards expected of refereed papers in professional journals or formal Agency reports. DocumentsIDA Documents are used for the convenience of the sponsors or the analysts (a) to record substantive work done in quick reaction studies, (b) to record the proceedings of conferences and meetings, (c) to make available preliminary and tentative results of analyses, (d) to record data developed in the course of an investigation, or (e) to forward information that is essentially unanalyzed and unevaluated. The review of IDA Documents is suited to their content and intended use.

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“…Such a result indicates that factors such as expectations and other sources of contextual scene information may be as important as image variance in determining performance in some situations. Birkmire, Karsh, Barnette, and Pillalamarri (1992) found that global SV was a poor predictor of overall search time. When SV was calculated for blocks of a display, Rotman, Kowalczyk, and George (1994) found that SV did not correlate highly with eye movements (i.e., fixations to regions of high clutter) in search.…”

Section: Williamsmentioning

confidence: 99%

“…Unfortunately, also like SV, the POE metric fails to accurately predict response time (Birkmire et al, 1992) and fixation location during search (Rotman et al, 1994a). Presumably, a problem with the metric is that although edges of objects lead to edge-defined pixels, edge-defined pixels do not necessarily indicate the edges of real objects.…”

Section: Williamsmentioning

confidence: 99%

Soldier-in-the-Loop Target Acquisition Performance Prediction Through 2001: Integration of Perceptual and Cognitive Models

Vaughan¹

2006

PsycEXTRA Dataset

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, ARL-TR-3833 SPONSOR/MONITOR'S ACRONYM(S) 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release; distribution is unlimited. SUPPLEMENTARY NOTES ABSTRACTModeling Soldier-in-the-loop target acquisition performance is necessary for the development of improved sensors, more effective training methods, and better war game simulations. Accurately modeling this performance requires a detailed understanding of the environment, how a sensor responds to the environment, how it displays information to an observer, and how the observer employs that information to acquire a target. The first three requirements have been met; the fourth requirement, however, has not yet been achieved. Attempts to model the observer's visual and decision-making processes have been compromised by the analysis of the scene, based on physical parameters alone rather than how the visual system interprets the scene. Models based on such scene-derived factors have had limited success.This report takes a two-pronged approach to how future models can be improved by the sensible integration of human visual processing. One prong concerns basic research from the perceptual psychology community. Over the last few decades, this research has generated a detailed theoretical understanding of visual processing and decision making, based on visual information. The other prong concerns important models, modeling frameworks, and scene metrics from the military target acquisition community. Particular attention is paid to issues of clutter, the extendibility of the Johnson criteria, classical and neoclassical search frameworks, the selection of methods and performance metrics, and existing Night Vision and Electronic Sensors Directorate models. Issues related to the validation of target acquisition models are also discussed. iii Abstract (continued)Existing target acquisition models tend to base performance on (a) one-dimensional (1-D) metrics defining the amount of information in the target (e.g., resolvable bar cycles, contrast, area, size, perimeter, speed of motion) and how that information correlates to level of performance in a target acquisition task (i.e., detection, classification, recognition, and identification), (b) search processes that are unrealistic (e.g., that assume random eye movements), and (c) 1-D metrics to define the whole scene (clutter) or regions of the scene (e.g., clutter, conspicuity, attractiveness). These tendencies fail to account for known human behavior, although models incorporating them may be insensitive t...

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Target Acquisition in Cluttered Environments

Cited by 9 publications

References 3 publications

DSIM: A DisSIMilarity-Based Image Clutter Metric for Targeting Performance

DSIM: A DisSIMilarity-Based Image Clutter Metric for Targeting Performance

A Search for Understanding. Analysis of Human Performance on Target Acquisition and Search Tasks using Eyetracker Data.

Soldier-in-the-Loop Target Acquisition Performance Prediction Through 2001: Integration of Perceptual and Cognitive Models

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