Classification images for aerial images capture visual expertise for binocular disparity and a prior for lighting from above

Abstract

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.

Publication DOI: https://doi.org/10.1167/jov.24.4.11
Divisions: College of Health & Life Sciences > School of Optometry > Optometry
College of Health & Life Sciences > School of Psychology
Aston University (General)
Funding Information: The authors would like to thank the Ordnance Survey for providing images and facilitating data collection for eight participants. We also thank the two reviewers for their insightful comments. E.S. was supported by funding from Aston University (UK) and O
Additional Information: Copyright © 2024, The Authors. This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
Uncontrolled Keywords: Humans,Vision Disparity,Lighting,Cues,Individuality,Learning,Sensory Systems,Ophthalmology
Publication ISSN: 1534-7362
Data Access Statement: The data supporting this work can be found at https://doi.org/10.17036/researchdata.aston.ac.uk.00000627.
Last Modified: 02 Dec 2024 09:05
Date Deposited: 30 Apr 2024 17:07
Full Text Link:
Related URLs: https://jov.arv ... ticleid=2793562 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2024-04-30
Published Online Date: 2024-04-12
Accepted Date: 2024-04-01
Authors: Skog, Emil
Meese, Timothy S. (ORCID Profile 0000-0003-3744-4679)
Sargent, Isabel M.J.
Ormerod, Andrew
Schofield, Andrew J. (ORCID Profile 0000-0002-0589-4678)

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