Influence of clutter on human target acquisition

Avital, Z.; Hadar, Yosi; Reiter, Gerhard; Tidhar, Gil; Rotman, Stanley R.; Grossman, Shlomit; Rehavi, Ada

doi:10.1117/12.151006

Cited by 5 publications

(3 citation statements)

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“…James A. Retches et al [28] thought that Clutter is a term borrowed from radar that has been expanded from the radar definition to include all signals in a scene of no interest to the observer, while Bhanu [29] defined clutter as an "object" which resembles a target, but is not a target. Although the background clutter still has no clear and unified concept, the effect caused by clutter on target acquisition performance is definite [30]. During target searching, the clutter will distract observers' attention.…”

Section: Definition Of Image Clutter Metricmentioning

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: Definition Of Image Clutter Metricmentioning

confidence: 99%

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

Shi

2013

IEEE Trans. on Image Process.

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“…A great deal of interest has recently been shown in defining clutter for electro-optical images and many definitions of clutter metrics have been proposed [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. The simplest and most widely used metric is the statistical variance (SV) [1], computed by averaging root-mean-square of the variance of contiguous square cells super-imposed on the images.…”

Section: Introductionmentioning

confidence: 99%

Detection probability and detection time using clutter metrics

Chang

Zhang

2007

Infrared Physics & Technology

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“…A great deal of interest has recently been shown in defining the relationship between image content and human detection performance. Much recent work in this vein has produced many different image metrics, [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] which were all designed to emphasize one or more parameters that dominate target detection by a human observer. In spite of so many definitions available, no single clutter definition has been agreed on by the infrared community as being the best.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of human detection performance using target structure similarity clutter metrics

Chang

Zhang

2006

Opt. Eng

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One of the most challenging research topics in the field of target detection in electro-optical images is the relationship between image content and human detection performance. We propose two target structure similarity ͑TSSIM͒ metrics to describe global image clutter. Via a simple mathematical formula, the TSSIM clutter metrics and their combinations with different local target-to-background contrasts, which are loosely referred to as signal-to-clutter ratios ͑SCRs͒, are used to predict human detection performance. Other clutter measures and their combinations with the contrast ones are also considered for comparison. The degrees of correlation between target detection probabilities and various clutter metrics, as well as the SCR models, are presented. Experiment results show that the TSSIM measures are more suitable for quantifying electro-optical clutter than other clutter measures compared. And the SCR metric, which combines the root-sum-of-squares local contrast and a TSSIM clutter metric, generates the best predictive capabilities for the experimental detection probabilities. © 2006 Society of Photo-Optical Instrumentation Engineers.

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Influence of clutter on human target acquisition

Cited by 5 publications

References 0 publications

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

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

Detection probability and detection time using clutter metrics

Evaluation of human detection performance using target structure similarity clutter metrics

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