&lt;title&gt;Clutter metrics for predicting human target acquisition performance&lt;/title&gt;

Rotman, Stanley R.; Aviram, Guy

doi:10.1117/12.440067

Cited by 8 publications

(4 citation statements)

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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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Section: Introductionmentioning

confidence: 99%

Detection probability and detection time using clutter metrics

Chang

Zhang

2007

Infrared Physics & Technology

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“…The POE (probability of edge) is a kind of global edge metric [9,10,11]. It is designed to measure image edges， which refers to the scene that contains information used to match.…”

Section: Information Content Analysismentioning

confidence: 99%

Infrared imaging characterization of ground scene

Zhang

Xu²

2009

SPIE Proceedings

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As prerequisite and foundation, the study of infrared imaging characterization for different ground scene in different time under different meteorological condition is the key of infrared image registration and ATR. In order to find out the proper time and optimum scene which is suitable for image registration, a great deal of aerial image sequences is analyzed in different season and different time at same region. Furthermore, a new metrics is put forward to measure the variation of IR scene with some statistical parameters, such as mean, standard deviation, POE. The metrics is designed to describe different scene properties quantitatively so as to evaluate the stability of scene MWIR imaging .According to the result of MWIR and visible image registration with NPROD, the proposed method can be used to correctly select reference area and increase the probability of map matching.

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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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<title>Clutter metrics for predicting human target acquisition performance</title>

Cited by 8 publications

References 0 publications

Detection probability and detection time using clutter metrics

Detection probability and detection time using clutter metrics

Infrared imaging characterization of ground scene

Evaluation of human detection performance using target structure similarity clutter metrics

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