Modeling human search and target acquisition performance: IV. detection probability in the cluttered environment

Tidhar, Gil; Reiter, Gerhard; Avital, Z.; Hadar, Yosi; Rotman, Stanley R.; George, Vivian; Kowalczyk, Marta

doi:10.1117/12.160980

Cited by 58 publications

(34 citation statements)

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“…Another metric that purports to extract meaningful information about a target from a description of edge-based information is the target complexity (TC) metric of Tidhar et al (1994). The metric is similar to the POE, but it adds the assumption that target objects will have more pronounced edges than interior details.…”

Section: Williamsmentioning

confidence: 99%

“…Generally speaking, the more such information is present at the target location, the greater the probability or possible level of acquisition. The constructs used by the models are typically onedimensional metrics such as conspicuity (e.g., Toet, 1996), number of resolvable cycles, N, of a bar pattern (i.e., a square wave) on a target (Johnson, 1958), or complexity (e.g., Tidhar et al, 1994). Such metrics may apply to the location of the target only or they may apply to the entire scene.…”

Section: Models Based On Physiology and Empirical Human Psychophysicsmentioning

confidence: 99%

“…One such metric that has been used in clutter and conspicuity metrics (e.g., Waldman, Wooton, Hobson, & Leutkemeyer, 1988;Tidhar et al, 1994; is the gray-level cooccurrency matrix. This matrix represents, within an area of a pixilated image, the frequency of one gray-level occurring in a specified linear spatial relationship with another gray-level.…”

Section: Williamsmentioning

confidence: 99%

“…(That the model arose from the discipline of machine vision may indicate that it is better suited for locating man-made objects in general than for predicting human search performance.) Tidhar et al's (1994) probability of edge (POE) clutter metric is founded on the idea that high spatial frequency edge information is important for the detection of targets. Related to this idea is the finding that the visual system seems to perform edge extraction early in visual processing, thereby creating a representation of the scene from which objects and surfaces can be readily extracted (Marr & Hilldreth, 1980;Marr, 1982;Nakayama & He, 1994;Biederman, 1987).…”

Section: Williamsmentioning

confidence: 99%

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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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Section: Williamsmentioning

confidence: 99%

Section: Models Based On Physiology and Empirical Human Psychophysicsmentioning

confidence: 99%

Section: Williamsmentioning

confidence: 99%

Section: Williamsmentioning

confidence: 99%

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

Vaughan¹

2006

PsycEXTRA Dataset

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“…The probability of edge metric (POE ) [38][39][40] is designed to imitate the human visual system, which is basically a band-pass filter and highly sensitive to image edges. The technical details of this metric are as follows: the given image is divided into blocks twice the size of the target in each dimension firstly; then a DOOG (Difference of Offset Gaussian) [41] operator is applied to each block to simulate one of the channels in preattentive human vision; after that, the net effect is used to enhance the edges; then, the resulting histogram of the processed scene is normalized to values between 0 and 255 for each scene, while the threshold is chosen empirically; finally, the fraction of points which pass the threshold T in the i-th block is computed as P OE i,T .…”

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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Optimization Tool for X-Ray Radiography NDE

Elshafiey

Gray

1996

Review of Progress in Quantitative Nondestructive Evaluation

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Modeling human search and target acquisition performance: IV. detection probability in the cluttered environment

Cited by 58 publications

References 0 publications

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

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

Image Sequence Measures for Automatic Target Tracking

Optimization Tool for X-Ray Radiography NDE

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