Evaluation of human detection performance using target structure similarity clutter metrics

Chang, Honghua; Zhang, Jianqi

doi:10.1117/1.2353848

Cited by 18 publications

(13 citation statements)

References 20 publications

(19 reference statements)

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“…And the sum of d P and f P is defined as the probability of total number of detections total P . That is to say, ) ( As is shown in paper [9], by using clutter metrics as predictor in the equation (11), we can rather well predict the probability of detection. …”

Section: Analysis and Resultsmentioning

confidence: 97%

Modeling human false alarms using clutter metrics

2007

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The TSSIM clutter metrics correlate amazingly well to both the experimental detection probabilities and the mean detection time. Based on the analysis of both probabilities of the correct detections and of the total (correct and false) number of detections made by human observers, a mathematical formula for predicting the probability of false alarms as a function of clutter metrics is presented in this paper. Comparing real experimental data with the predicted products reveal very good agreement, which is very helpful in understanding human behavior mechanisms regarding target detection tasks. It is concluded that the human observer behaves as fixed threshold signal processor /Non-CFAR.

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Section: Analysis and Resultsmentioning

confidence: 97%

Modeling human false alarms using clutter metrics

2007

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“…6,7,11 Given such sparse datasets, it is worthwhile to also investigate the mathematical behavior of clutter metrics. Results have been published drawing conclusions from tests using only hundreds or even tens of scenes.…”

Section: Shortcomings Of Some Prior Clutter Metricsmentioning

confidence: 99%

“…[3][4][5][6][7][8][9][10][11][12] Recently, the masked target transform volume (MTTV) metric has provided promising results that overcome some limitations of earlier metrics. [3][4][5][6][7][8][9][10][11][12] Recently, the masked target transform volume (MTTV) metric has provided promising results that overcome some limitations of earlier metrics.…”

Section: Introductionmentioning

confidence: 99%

Performance characteristics of the masked target transform volume clutter metric

Camp¹,

Moyer

Moore

2012

Opt. Eng

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Prior work has shown that the masked target transform volume (MTTV) clutter metric provides a measure of scene clutter that better correlates to measured probability of detection for human observers than several previously published clutter metrics. Several factors involved in using the MTTV to assess clutter in imagery are discussed here. A previously published modification to the MTTV metric to provide a normalized output value comparable across different image sets regardless of scene size is reviewed. Initial MTTV development required knowledge of a scene's target signature and produced an unbounded metric value. Metric behavior is discussed for the case in which an average of several target signatures is used in place of a specific target signature. This allows the MTTV to be calculated for images that do not contain a target. It is shown that the user may trade computational efficiency with metric accuracy to suit a particular application. The sensitivity of the metric to variations in image noise level, target segmentation error, and viewing distance are also presented.

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“…1 Thus, a great deal of research [1][2][3][4][5][6][7][8][9][10][11][12] has concentrated on the relationship between background clutter and human detection performance. As for the target structure similarity measure (TSSIM), 6 its performance is dependent on the selection of the two constants, C 1 and C 2 . However, it is too simple to describe the complex background and is very sensitive to the magnitude of the edges.…”

Section: Introductionmentioning

confidence: 99%

Contrast-sensitivity-function-based clutter metric

Chu

Yang

2012

Opt. Eng

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Background clutter can obscure or mimic the desired target and confuse the operator. Thus, the similarity between the background and the target is a typical feature of the background clutter. A clutter metric based on human contrast sensitivity function is proposed. It measures the similarity between the background and the target in the frequency domain, after the differences that cannot be seen by the visual system have been "thrown away" and the visible differences have been weighted according to the sensitivity of the visual system. The correlation between the experimental probability of detection and that predicted by the clutter metric are presented. Experiment results show that the contrastsensitivity-function-based clutter metric can perform other clutter metrics in open literature in quantifying background clutter. Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 05/14/2015 Terms of Use: http://spiedl.org/terms Optical Engineering 067003-2 June 2012/Vol. 51(6) Chu, Yang, and Li: Contrast-sensitivity-function-based clutter metric Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 05/14/2015 Terms of Use: http://spiedl.org/terms

show abstract

Evaluation of human detection performance using target structure similarity clutter metrics

Cited by 18 publications

References 20 publications

Modeling human false alarms using clutter metrics

Modeling human false alarms using clutter metrics

Performance characteristics of the masked target transform volume clutter metric

Contrast-sensitivity-function-based clutter metric

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