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Classification imag...
Classification images for aerial images capture visual expertise for binocular disparity and a prior for lighting from above
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- Skog, Emil (author)
- Luleå tekniska universitet,Hälsa, medicin och rehabilitering,School of Psychology, College of Health and Life Sciences, Aston University, Birmingham, B4 7ET, UK; Aston Laboratory for Immersive Virtual Environments, College of Health and Life Sciences, Aston University, Birmingham, B4 7ET, UK
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- Meese, Timothy S. (author)
- Aston Laboratory for Immersive Virtual Environments, College of Health and Life Sciences, Aston University, Birmingham, B4 7ET, UK
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- Sargent, Isabel M. J. (author)
- Ordnance Survey, Adanac Drive, Southampton, SO16 0AS, UK; Electronics and Computer Science, University of Southampton, University Road, Southampton, SO17 1BJ, UK
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- Ormerod, Andrew (author)
- Ordnance Survey, Adanac Drive, Southampton, SO16 0AS, UK
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- Schofield, Andrew J. (author)
- School of Psychology, College of Health and Life Sciences, Aston University, Birmingham, B4 7ET, UK; Aston Laboratory for Immersive Virtual Environments, College of Health and Life Sciences, Aston University, Birmingham, B4 7ET, UK
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(creator_code:org_t)
- Association for Research in Vision and Ophthalmology, 2024
- 2024
- English.
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In: Journal of Vision. - : Association for Research in Vision and Ophthalmology. - 1534-7362. ; 24:4
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https://ltu.diva-por... (primary) (Raw object)
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Abstract
Subject headings
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- Using a novel approach to classification images (CIs), we investigated the visual expertise of surveyors for luminance and binocular disparity cues simultaneously after screening for stereoacuity. Stereoscopic aerial images of hedges and ditches were classified in 10,000 trials by six trained remote sensing surveyors and six novices. Images were heavily masked with luminance and disparity noise simultaneously. Hedge and ditch images had reversed disparity on around half the trials meaning hedges became ditch-like and vice versa. The hedge and ditch images were also flipped vertically on around half the trials, changing the direction of the light source and completing a 2 × 2 × 2 stimulus design. CIs were generated by accumulating the noise textures associated with “hedge” and “ditch” classifications, respectively, and subtracting one from the other. Typical CIs had a central peak with one or two negative side-lobes. We found clear differences in the amplitudes and shapes of perceptual templates across groups and noise-type, with experts prioritizing binocular disparity and using this more effectively. Contrariwise, novices used luminance cues more than experts meaning that task motivation alone could not explain group differences. Asymmetries in the luminance CIs revealed individual differences for lighting interpretation, with experts less prone to assume lighting from above, consistent with their training on aerial images of UK scenes lit by a southerly sun. Our results show that (i) dual noise in images can be used to produce simultaneous CI pairs, (ii) expertise for disparity cues does not depend on stereoacuity, (iii) CIs reveal the visual strategies developed by experts, (iv) top-down perceptual biases can be overcome with long-term learning effects, and (v) CIs have practical potential for directing visual training.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Keyword
- Psychology
- Psykologi
Publication and Content Type
- ref (subject category)
- art (subject category)
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