2010
DOI: 10.1167/10.2.23
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(In) Sensitivity to spatial distortion in natural scenes

Abstract: The perception of object structure in the natural environment is remarkably stable under large variation in image size and projection, especially given our insensitivity to spatial position outside the fovea. Sensitivity to periodic spatial distortions that were introduced into one quadrant of gray-scale natural images was measured in a 4AFC task. Observers were able to detect the presence of distortions in unfamiliar images even though they did not significantly affect the amplitude spectrum. Sensitivity depe… Show more

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Cited by 63 publications
(54 citation statements)
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“…Spatial distortions were smoothly blended into the image with a Gaussian window with a standard deviation of 1°, thus the magnitude of distortion beyond a 2° radius (±2σ) approached 0, which ensured that there were no abrupt transitions between distorted and undistorted areas of the image. The magnitude of the spatial distortion and the size of the Gaussian window were based on data from a recent study 12 in which we measured detection thresholds for spatial distortions in natural scenes across the visual field. The magnitude (0.5°) and spatial period (2°) of spatial distortion employed in the present study were based on those data were above detection threshold at all eccentricities and should therefore be detectable by peripheral vision with artificial central vision loss.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Spatial distortions were smoothly blended into the image with a Gaussian window with a standard deviation of 1°, thus the magnitude of distortion beyond a 2° radius (±2σ) approached 0, which ensured that there were no abrupt transitions between distorted and undistorted areas of the image. The magnitude of the spatial distortion and the size of the Gaussian window were based on data from a recent study 12 in which we measured detection thresholds for spatial distortions in natural scenes across the visual field. The magnitude (0.5°) and spatial period (2°) of spatial distortion employed in the present study were based on those data were above detection threshold at all eccentricities and should therefore be detectable by peripheral vision with artificial central vision loss.…”
Section: Methodsmentioning
confidence: 99%
“…If the location of the target and non-target features were entirely random prior knowledge of the stimulus layout could not influence oculomotor behavior 11 . Alternatively, replacing the search target with that of a random spatial distortion does not change natural image statistics (local spatial structure, luminance, contrast, edge density or amplitude spectrum) in a predictable way 12 . This avoids the introduction of abrupt changes in luminance, chrominance or contrast and prevents the use of a single filter to make the task unnaturally simple– e.g.…”
mentioning
confidence: 99%
“…Human sensitivity to spatial distortions has been investigated previously in images of faces (Spence et al, 2014;Rovamo et al, 1997;Dickinson et al, 2010;Hole et al, 2002) and natural scenes (Kingdom et al, 2007;Bex, 2010). To our knowledge, only one study has assessed the impact of spatial distortion for letter stimuli.…”
mentioning
confidence: 99%
“…We quantify the detectability of two different types of spatial distortion commonly used in the literature (see also Stojanoski & Cusack, 2014, for another distortion not employed here). In bandpass noise distortions (hereafter referred to as BPN distortion; Bex, 2010), pixels are warped according to bandpass filtered noise; this ensures that the distortion occurs on a defined and limited spatial scale. In radial frequency distortions (hereafter referred to as RF distortion; Dickinson et al, 1998;Wilkinson, Wilson, & Habak, 2010), the image is warped by modulating the radius (defined from the image center) according to a sinusoidal function of some frequency defined in polar coordinates.…”
mentioning
confidence: 99%
“…In contrast, Wallis et al (2017) used image patches that were 128×128 pixels to compare between texture images created from a deep neural network model and real photographs of textures. Other studies have used a range of images sizes (Alam et al, 2014;Sebastian et al, 2017;Bex, 2010, in increasing order of image size), but our images are closer to the range of image sizes used as patches of images (Gerhard et al, 2013) rather than entire images. However, training generative adversarial networks on larger images with similar image variability as the CIFAR10 network currently typically requires training class conditional networks as for example done by Miyato et al (2018) when training on the entire ImageNet dataset (Russakovsky et al, 2015).…”
Section: Small Imagesmentioning
confidence: 99%