Evaluation of human detection performance of targets embedded in natural and enhanced infrared images using image metrics

Aviram, Guy; Rotman, Stanley R.

doi:10.1117/1.602441

Cited by 27 publications

(21 citation statements)

References 12 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: 81%

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: 81%

Urban camouflage assessment through visual search and computational saliency

Toet

Hogervorst

2012

Opt. Eng

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“…(9)- (11), and setting all stabilization constants to zero, we rewrite Eq. (12) in its original full form (Eq.…”

Section: The Tssim In Its Full Formmentioning

confidence: 99%

“…Many attempts have been made to quantify the effects of clutter by means of digital clutter metrics. However, since the concept is inherently elusive, attempts to model clutter have only been partly successful [3][4][5][6][6][7][8][9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Structural similarity determines search time and detection probability

Toet¹

2010

Infrared Physics & Technology

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“…With further understanding the connotation of an image clutter metric, many image clutter metrics have been proposed based on perceptual properties of the human visual system (HVS). They can be roughly divided into three kinds: the first kind are based on the sensitivity of the HVS to the areas with high contrast, such as, probability of edge (POE) metric [10], entropy of edge (EOE) metric [11], and peak signal (PS) metric [12]. This kind of clutter metrics correlates well with human performance measures.…”

Section: Introductionmentioning

confidence: 99%