Evaluating human detection performance of targets and false alarms, using a statistical texture image metric

Aviram, Guy; Rotman, Stanley R.

doi:10.1117/1.1304925

Cited by 27 publications

(20 citation statements)

References 14 publications

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“…The latter has been investigated in laboratory experiments using artificially patterned prey and backgrounds, with birds [14,21,22] and fish [23]. There is value in replicating experiments on the effects of background complexity under more natural conditions; natural textures differ from the types of artificial textures used in these experiments in many ways, such as the contrast range and how deterministic or periodic the pattern is [24]. Importantly, although these studies have demonstrated effects of background 'complexity' ('high variability or complexity in shapes of the elements constituting the background' [21]), it is not clear which perceptual aspects of complexity interfere with visual search, nor how one could translate this, intuitively reasonable, verbal description into a numerical measure of complexity.…”

Section: Introductionmentioning

confidence: 99%

Background complexity and the detectability of camouflaged targets by birds and humans

Xiao¹,

Cuthill²

2016

Proc. R. Soc. B.

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Remaining undetected is often key to survival, and camouflage is a widespread solution. However, extrinsic to the animal itself, the complexity of the background may be important. This has been shown in laboratory experiments using artificially patterned prey and backgrounds, but the mechanism remains obscure (not least because 'complexity' is a multifaceted concept). In this study, we determined the best predictors of detection by wild birds and human participants searching for the same cryptic targets on trees in the field. We compared detection success to metrics of background complexity and 'visual clutter' adapted from the human visual salience literature. For both birds and humans, the factor that explained most of the variation in detectability was the textural complexity of the tree bark as measured by a metric of feature congestion (specifically, many nearby edges in the background). For birds, this swamped any effects of colour match to the local surroundings, although for humans, local luminance disparities between the target and tree became important. For both taxa, a more abstract measure of complexity, entropy, was a poorer predictor. Our results point to the common features of background complexity that affect visual search in birds and humans, and how to quantify them.

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Section: Introductionmentioning

confidence: 99%

Background complexity and the detectability of camouflaged targets by birds and humans

Xiao¹,

Cuthill²

2016

Proc. R. Soc. B.

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“…This metric incorporates the attributes of global texture matching and of local texture distinctness, by analyzing the texture differences between the target and suspected target areas and also between suspected target areas and their local background. The technical details of the ICOM are somewhat complicated and can be found in reference [45].…”

Section: Target Dependent Metricsmentioning

confidence: 99%

“…Based on the theory of COM, Aviram and Rotman developed a new texture metric which is called improved co-occurrence matrix (ICOM ) [45]. This metric incorporates the attributes of global texture matching and of local texture distinctness, by analyzing the texture differences between the target and suspected target areas and also between suspected target areas and their local background.…”

Section: Target Dependent Metricsmentioning

confidence: 99%

“…The valid region for calculating inter-frame change of texture includes target region and local background surrounding the target. Specifically, the region is defined as the rectangle which area is twice the target area according to [45,53] and [54], as shown in Fig. 3.…”

Section: Inter-frame Change Degree Of Texture (Ifcdt )mentioning

confidence: 99%

“…One typical metric is the DOYLE metric (named after L. Doyle) [45], which combines target and background means and variances of pixel values. The basic form of the DOYLE metric is…”

Section: Target Dependent Metricsmentioning

confidence: 99%

See 2 more Smart Citations

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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Target Classification Using Adaptive Feature Extraction and Subspace Projection for Hyperspectral Imagery

Kwon

Der

Nasrabadi

Advances in Pattern Recognition

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Evaluating human detection performance of targets and false alarms, using a statistical texture image metric

Cited by 27 publications

References 14 publications

Background complexity and the detectability of camouflaged targets by birds and humans

Background complexity and the detectability of camouflaged targets by birds and humans

Image Sequence Measures for Automatic Target Tracking

Target Classification Using Adaptive Feature Extraction and Subspace Projection for Hyperspectral Imagery

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