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Detection of occluding targets in natural backgrounds

Detection of target objects in the surrounding environment is a common visual task. There is a vast psychophysical and modeling literature concerning the detection of targets in artificial and natural backgrounds. Most studies involve detection of additive targets or of some form of image distortion...

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Autores principales: Walshe, R. Calen, Geisler, Wilson S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association for Research in Vision and Ophthalmology 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7774104/
https://www.ncbi.nlm.nih.gov/pubmed/33355596
http://dx.doi.org/10.1167/jov.20.13.14
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author Walshe, R. Calen
Geisler, Wilson S.
author_facet Walshe, R. Calen
Geisler, Wilson S.
author_sort Walshe, R. Calen
collection PubMed
description Detection of target objects in the surrounding environment is a common visual task. There is a vast psychophysical and modeling literature concerning the detection of targets in artificial and natural backgrounds. Most studies involve detection of additive targets or of some form of image distortion. Although much has been learned from these studies, the targets that most often occur under natural conditions are neither additive nor distorting; rather, they are opaque targets that occlude the backgrounds behind them. Here, we describe our efforts to measure and model detection of occluding targets in natural backgrounds. To systematically vary the properties of the backgrounds, we used the constrained sampling approach of Sebastian, Abrams, and Geisler (2017). Specifically, millions of calibrated gray-scale natural-image patches were sorted into a 3D histogram along the dimensions of luminance, contrast, and phase-invariant similarity to the target. Eccentricity psychometric functions (accuracy as a function of retinal eccentricity) were measured for four different occluding targets and 15 different combinations of background luminance, contrast, and similarity, with a different randomly sampled background on each trial. The complex pattern of results was consistent across the three subjects, and was largely explained by a principled model observer (with only a single efficiency parameter) that combines three image cues (pattern, silhouette, and edge) and four well-known properties of the human visual system (optical blur, blurring and downsampling by the ganglion cells, divisive normalization, intrinsic position uncertainty). The model also explains the thresholds for additive foveal targets in natural backgrounds reported in Sebastian et al. (2017).
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spelling pubmed-77741042021-01-13 Detection of occluding targets in natural backgrounds Walshe, R. Calen Geisler, Wilson S. J Vis Article Detection of target objects in the surrounding environment is a common visual task. There is a vast psychophysical and modeling literature concerning the detection of targets in artificial and natural backgrounds. Most studies involve detection of additive targets or of some form of image distortion. Although much has been learned from these studies, the targets that most often occur under natural conditions are neither additive nor distorting; rather, they are opaque targets that occlude the backgrounds behind them. Here, we describe our efforts to measure and model detection of occluding targets in natural backgrounds. To systematically vary the properties of the backgrounds, we used the constrained sampling approach of Sebastian, Abrams, and Geisler (2017). Specifically, millions of calibrated gray-scale natural-image patches were sorted into a 3D histogram along the dimensions of luminance, contrast, and phase-invariant similarity to the target. Eccentricity psychometric functions (accuracy as a function of retinal eccentricity) were measured for four different occluding targets and 15 different combinations of background luminance, contrast, and similarity, with a different randomly sampled background on each trial. The complex pattern of results was consistent across the three subjects, and was largely explained by a principled model observer (with only a single efficiency parameter) that combines three image cues (pattern, silhouette, and edge) and four well-known properties of the human visual system (optical blur, blurring and downsampling by the ganglion cells, divisive normalization, intrinsic position uncertainty). The model also explains the thresholds for additive foveal targets in natural backgrounds reported in Sebastian et al. (2017). The Association for Research in Vision and Ophthalmology 2020-12-23 /pmc/articles/PMC7774104/ /pubmed/33355596 http://dx.doi.org/10.1167/jov.20.13.14 Text en Copyright 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
spellingShingle Article
Walshe, R. Calen
Geisler, Wilson S.
Detection of occluding targets in natural backgrounds
title Detection of occluding targets in natural backgrounds
title_full Detection of occluding targets in natural backgrounds
title_fullStr Detection of occluding targets in natural backgrounds
title_full_unstemmed Detection of occluding targets in natural backgrounds
title_short Detection of occluding targets in natural backgrounds
title_sort detection of occluding targets in natural backgrounds
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7774104/
https://www.ncbi.nlm.nih.gov/pubmed/33355596
http://dx.doi.org/10.1167/jov.20.13.14
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