On the detection of signals embedded in natural scenes

Caelli, Terry; Moraglia, Giampaolo

doi:10.3758/bf03211490

Cited by 32 publications

(17 citation statements)

References 11 publications

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“…Then we showed that it is the structural similarity component of the TSSIM that predicts both mean search time and detection probability, while the luminance and contrast similarity components do not correlate with human observer performance. This result agrees with the related observations that the cross-correlation component of the SSIM predicts visual image quality 23 , and that human observers mainly rely on structural features to recognize image content 5,7,8,21,22 . Furthermore, it should be noted that the structural similarity component of the TSSIM is equivalent to a matched filter.…”

Section: Discussionsupporting

confidence: 92%

“…Visual search experiments have shown that detection performance depends mainly on the energy contrast between a target and its local background, whereas recognition depends mainly on the structural dissimilarity between a target and its surround 5,7 . For complex scenes, the spatial relationships (shape and relative location) of features in an image can have a greater effect on detection than the relative luminance of the features 8 .…”

Section: Introductionmentioning

confidence: 99%

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Matched filtering determines human visual search in natural images

Toet

2011

SPIE Proceedings

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The structural image similarity index (SSIM), introduced by Wang and Bovik (IEEE Signal Processing Letters 9-3, pp. 81-84, 2002) measures the similarity between images in terms of luminance, contrast en structure. It has successfully been deployed to model human visual perception of image distortions and modifications in a wide range of different imaging applications. Chang and Zhang (Infrared Physics & Technology 51-2, pp. 83-90, 2007) recently introduced the target structural similarity (TSSIM) clutter metric, which deploys the SSIM to quantify the similarity of a target to its background in terms of luminance, contrast en structure. They showed that the TSSIM correlates significantly with mean search time and detection probability. However, it is not immediately obvious to what extent each of the three TSSIM components contributes to this correlation. Here we evaluate the TSSIM by deploying it to a set of natural images for which human visual search data are available: the Search_2 dataset. By analyzing the predictive performance of each of the three TSSIM components, we find that it is predominantly the structural similarity component which determines human visual search performance, whereas the luminance and contrast components of the TSSIM show no relation with human performance. Since the structural similarity component of the TSSIM is equivalent to a matched filter, it appears that matched filtering predicts human visual performance when searching for a known target.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Matched filtering determines human visual search in natural images

Toet

2011

SPIE Proceedings

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“…It appears that detection performance depends to a large degree on the energy contrast between a target and its local background, whereas recognition depends mainly on the structural dissimilarity between a target and its surround. 52,53 This obscuring effect, which is generally known as clutter, determines human visual search and detection performance to a large extent. For complex scenes, the spatial relationships (shape and relative location) of features in an image can have a greater effect on detection than the relative luminance of the features.…”

Section: Discussionmentioning

confidence: 99%

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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“…Although there have been a number of studies of detection in natural backgrounds (16)(17)(18)(19)(20)(21)(22)(23), they have not directly addressed these questions, and have either tested only a small number of natural stimuli (16,17,19,20), tested natural stimuli with altered statistical properties (21,22), or used experimental paradigms not representative of natural detection tasks (16,(18)(19)(20)23). These latter studies are not as representative of natural tasks, because observers were allowed to directly compare the same image with and without the added target, an advantage that is not normally available under real-world conditions.…”

mentioning

confidence: 99%

Constrained sampling experiments reveal principles of detection in natural scenes

Sebastian

Abrams

Geisler

2017

Proc. Natl. Acad. Sci. U.S.A.

124

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A fundamental everyday visual task is to detect target objects within a background scene. Using relatively simple stimuli, vision science has identified several major factors that affect detection thresholds, including the luminance of the background, the contrast of the background, the spatial similarity of the background to the target, and uncertainty due to random variations in the properties of the background and in the amplitude of the target. Here we use an experimental approach based on constrained sampling from multidimensional histograms of natural stimuli, together with a theoretical analysis based on signal detection theory, to discover how these factors affect detection in natural scenes. We sorted a large collection of natural image backgrounds into multidimensional histograms, where each bin corresponds to a particular luminance, contrast, and similarity. Detection thresholds were measured for a subset of bins spanning the space, where a natural background was randomly sampled from a bin on each trial. In low-uncertainty conditions, both the background bin and the amplitude of the target were fixed, and, in high-uncertainty conditions, they varied randomly on each trial. We found that thresholds increase approximately linearly along all three dimensions and that detection accuracy is unaffected by background bin and target amplitude uncertainty. The results are predicted from first principles by a normalized matched-template detector, where the dynamic normalizing gain factor follows directly from the statistical properties of the natural backgrounds. The results provide an explanation for classic laws of psychophysics and their underlying neural mechanisms.natural scene statistics | detection | masking | normalization | Weber's law V isual systems are the result of evolution by natural selection, and, as a consequence, their design is strongly constrained by the properties of natural visual stimuli and by the specific visual tasks performed to survive and reproduce. Thus, to understand the human visual system, it is critical to characterize natural visual stimuli and performance in natural visual tasks (1, 2).Perhaps the most fundamental visual task is to identify target objects in the natural backgrounds that surround us. It is known that the specific properties of a background can have a strong influence on detectability (Fig. 1). For example, the detectability of a target pattern with given amplitude decreases with increases in background luminance (3, 4), background contrast (5-7), and the similarity of the spatial properties of the background to those of the target (8-12). In addition to the direct effects of such background properties, there are other factors that affect detection performance. Specifically, under natural conditions, the strength (amplitude) and location of the target often randomly vary on every occasion, and the target typically appears against a different background scene on every occasion. The uncertainty created by the random amplitude and location of the target ("target...

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On the detection of signals embedded in natural scenes

Cited by 32 publications

References 11 publications

Matched filtering determines human visual search in natural images

Matched filtering determines human visual search in natural images

Urban camouflage assessment through visual search and computational saliency

Constrained sampling experiments reveal principles of detection in natural scenes

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