Clutter metrics for target detection systems

Rotman, Stanley R.; Tidhar, Gil; Kowalczyk, Marta

doi:10.1109/7.250408

Cited by 42 publications

(19 citation statements)

References 15 publications

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“…However, the concept of clutter is inherently elusive, and attempts to model it have only been partly successful. [54][55][56][57][58][59][60][61][62][63][64][65][66][67][68] The present results indicate that the simple computational FTS saliency metric predicts the relative effectiveness of different visual camouflage designs to a large degree. However, this metric only incorporates the energy contrast between a target and its local background, and therefore only models human detection performance.…”

Section: Discussionmentioning

confidence: 80%

Urban camouflage assessment through visual search and computational saliency

Toet

Hogervorst

2012

Opt. Eng

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Abstract. We present a new method to derive a multiscale urban camouflage pattern from a given set of background image samples. We applied this method to design a camouflage pattern for a given (semi-arid) urban environment. We performed a human visual search experiment and a computational evaluation study to assess the effectiveness of this multiscale camouflage pattern relative to the performance of 10 other (multiscale, disruptive and monotonous) patterns that were also designed for deployment in the same operating theater. The results show that the pattern combines the overall lowest detection probability with an average mean search time. We also show that a frequency-tuned saliency metric predicts human observer performance to an appreciable extent. This computational metric can therefore be incorporated in the design process to optimize the effectiveness of camouflage patterns derived from a set of background samples.

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Section: Discussionmentioning

confidence: 80%

Urban camouflage assessment through visual search and computational saliency

Toet

Hogervorst

2012

Opt. Eng

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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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“…[1,2,3,4,5,6] Some of these metrics are listed below; mean radiance or ΔT metric statistical variance metric probability of edge metric complexity metrics There is no definition which is clearly the best in all cases for the ATR community. The object of this paper is to give a unified definition of clutter which takes into account the different definitions available in the literature.…”

Section: Introductionmentioning

confidence: 99%