September 2015
Volume 15, Issue 12
Free
Vision Sciences Society Annual Meeting Abstract  |   September 2015
Perception of 3D structure and natural scene statistics: The Southampton-York Natural Scenes (SYNS) dataset.
Author Affiliations
  • Wendy Adams
    Psychology, University of Southampton, UK
  • James Elder
    Centre for Vision Research, York University, Canada
  • Erich Graf
    Psychology, University of Southampton, UK
  • Alex Muryy
    Psychology, University of Southampton, UK
  • Arthur Lugtigheid
    Psychology, University of Southampton, UK
Journal of Vision September 2015, Vol.15, 726. doi:10.1167/15.12.726
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      Wendy Adams, James Elder, Erich Graf, Alex Muryy, Arthur Lugtigheid; Perception of 3D structure and natural scene statistics: The Southampton-York Natural Scenes (SYNS) dataset.. Journal of Vision 2015;15(12):726. doi: 10.1167/15.12.726.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

We are interested in the relationship between human vision and the environment in which it operates. To this end, the University of Southampton (UK) and York University (Canada) have collaborated to build the Southampton-York Natural Scenes (SYNS) public dataset. To represent the diverse environments that humans experience, we sampled scenes from 19 outdoor and 6 indoor scene categories across Hampshire, UK. Outdoor categories, identified by the UK Land Use dataset, include cropland, coastal dunes, woodlands, industrial estates, wetlands, residential areas, farms and orchards. Indoor categories include residential, theatres, cafes and offices. Each scene is represented by three types of co-registered data: (i) Ground truth 3D structure: 360° x 135° depth maps from a laser rangefinder (LiDAR), (ii) High dynamic range images (360° x 180°) captured by a SpheroCam and (iii) 18 Stereo image pairs (35° x 24°), tiling a 360° horizontal panorama, captured by a custom-built high-resolution stereo rig, with camera separation matched to average human interpupillary distance. LiDAR data were analysed to determine the distribution of surface attitude over slant and tilt in natural scenes. Surface normals were computed for patches centred on each LiDAR point, with the optimal patch size determined by cross-validation. Overall, the joint distribution over slant and tilt is dominated by the ground plane. For elevations above the horizon, other regularities are also apparent, including elevated probability density at the cardinal tilt axes (vertical surfaces), and a peak at fronto-parallel, as predicted by the geometry of projection. We relate these natural scene statistics to human perception of surface attitude and find a general correspondence, with human tilt perception biased toward the ground plane and slant perception biased toward fronto-parallel. These results suggest that human perception of surface attitude is governed in part by the ecological statistics of our visual environment.

Meeting abstract presented at VSS 2015

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