The Southampton-York Natural Scenes (SYNS) dataset: statistics of surface attitude


Adams, Wendy, Elder, James and Graf, Erich et al. (2016) The Southampton-York Natural Scenes (SYNS) dataset: statistics of surface attitude Scientific Reports, 6, (35805), pp. 1-16. (doi:10.1038/srep35805).

Download

[img] PDF AdamsElderGrafEtAl2016SciReports.pdf - Version of Record
Available under License Creative Commons Attribution.

Download (2MB)

Description/Abstract

Recovering 3D scenes from 2D images is an under-constrained task; optimal estimation depends upon knowledge of the underlying scene statistics. Here we introduce the Southampton-York Natural Scenes dataset (SYNS: https://syns.soton.ac.uk), which provides comprehensive scene statistics useful for understanding biological vision and for improving machine vision systems. In order to capture the diversity of environments that humans encounter, scenes were surveyed at random locations within 25 indoor and outdoor categories. Each survey includes (i) spherical LiDAR range data (ii) high-dynamic range spherical imagery and (iii) a panorama of stereo image pairs. We envisage many uses for the dataset and present one example: an analysis of surface attitude statistics, conditioned on scene category and viewing elevation. Surface normals were estimated using a novel adaptive scale selection algorithm. Across categories, surface attitude below the horizon is dominated by the ground plane (0° tilt). Near the horizon, probability density is elevated at 90°/270° tilt due to vertical surfaces (trees, walls). Above the horizon, probability density is elevated near 0° slant due to overhead structure such as ceilings and leaf canopies. These structural regularities represent potentially useful prior assumptions for human and machine observers, and may predict human biases in perceived surface attitude.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1038/srep35805
Organisations: Psychology
ePrint ID: 401886
Date :
Date Event
5 October 2016Accepted/In Press
26 October 2016Published
Date Deposited: 27 Oct 2016 08:05
Last Modified: 17 Apr 2017 01:16
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/401886

Actions (login required)

View Item View Item