Detection Performance in Clutter with Variable Resolution

Schmieder, David E.; Weathersby, Marshall R.

doi:10.1109/taes.1983.309351

Cited by 130 publications

(78 citation statements)

References 4 publications

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“…[5,6] He found that the amount and nature of background clutter had a significant impact on the probability of target detection. Schmieder first looked at scene radiance standard deviation as a clutter measure.…”

Section: Taws Spm and Methodologymentioning

confidence: 99%

“…However, this measure had the deficiency of giving large clutter values to relatively uncluttered scenes when those scenes possessed several intensity modes. Moreover, this definition, like many other amplitude measures [5], lacked a weighting factor based on target size. Both amplitude and target size measures appeared to be required to predict observed trends.…”

Section: Taws Spm and Methodologymentioning

confidence: 99%

“…The target discrimination MRT methodology, based on the Johnson cycle criteria [4] in Acquire and Schmieder's criteria [5,6] in the TAWS, can be used for the prediction of target-acquisition range at discrimination levels of detection, recognition, and identification. Both the TAWS and Acquire can use MDT to predict detection range also.…”

Section: Sensor Performance Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Weather Effects on Target Acquisition Part 1: Sensor Performance Model Infrared Algorithms

Shirkey¹,

Sauter²,

Cormier³

2001

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NOTICES DisclaimersThe findings in this report are not to be construed as an official Department of the Army position, unless so designated by other authorized documents.Citation of manufacturers' or trade names does not constitute an official endorsement or approval of the use thereof.REPORT 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 viewing the collection information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction (0704-0188), Washington, DC_20503_

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Section: Taws Spm and Methodologymentioning

confidence: 99%

Section: Taws Spm and Methodologymentioning

confidence: 99%

Section: Sensor Performance Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Weather Effects on Target Acquisition Part 1: Sensor Performance Model Infrared Algorithms

Shirkey¹,

Sauter²,

Cormier³

2001

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“…Schmieder and Weathersby considered a metric C for clutter [37]. They proposed to divide the scene into N blocks, where the length of the block in each dimension is twice the size of the target.…”

Section: Target Independent Metricsmentioning

confidence: 99%

Image Sequence Measures for Automatic Target Tracking

Diao¹,

Mao²,

Zheng³

et al. 2012

PIER

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Abstract-In the field of automatic target recognition and tracking, traditional image metrics focus on single images, ignoring the sequence information of multiple images. We show that measures extracted from image sequences are highly relevant concerning the performances of automatic target tracking algorithms. To compensate the current lack of image sequence characterization systems from the perspective of the target tracking difficulties, this paper proposes three new metrics for measuring image sequences: inter-frame change degree of texture, inter-frame change degree of target size and inter-frame change degree of target location. All are based on the fact that inter-frame change is the main cause interfering with target tracking in an image sequence. As image sequences are an important type of data in the field of automatic target recognition and tracking, it can be concluded that the work in this paper is a necessary supplement for the existing image measurement systems. Experimental results reported show that the proposed metrics are valid and useful.

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“…Fixations tend to be executed to "target-like" regions of the scene and not to locations at random. The presence of many target-like objects in the field is also known to increase the false detection probability compared to when there is relatively little clutter (Schmieder & Weathersby, 1983).…”

Section: Clutter and Its Impact On Performancementioning

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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Detection Performance in Clutter with Variable Resolution

Cited by 130 publications

References 4 publications

Weather Effects on Target Acquisition Part 1: Sensor Performance Model Infrared Algorithms

Weather Effects on Target Acquisition Part 1: Sensor Performance Model Infrared Algorithms

Image Sequence Measures for Automatic Target Tracking

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

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