August 2016
Volume 16, Issue 12
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2016
Natural scene statistics and estimation of shape and reflectance.
Author Affiliations
  • Wendy Adams
    University of Southampton
  • Erich W. Graf
    University of Southampton
  • James H. Elder
    York University, Canada
Journal of Vision September 2016, Vol.16, 6. doi:10.1167/16.12.6
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      Wendy Adams, Erich W. Graf, James H. Elder; Natural scene statistics and estimation of shape and reflectance.. Journal of Vision 2016;16(12):6. doi: 10.1167/16.12.6.

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      © 2017 Association for Research in Vision and Ophthalmology.

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Abstract

A major function of the visual system is to estimate the shape and reflectance of objects and surfaces from the image. Evidence from both human and computer vision suggests that solutions to this problem involve exploiting prior probability distributions over shape, reflectance and illumination. In an optimal system, these priors would reflect the statistics of our world. To allow a better understanding of the statistics of our environment, and how these statistics shape human perception, we have developed the Southampton-York Natural Scenes (SYNS) public dataset. The dataset includes scene samples from a wide variety of indoor and outdoor scene categories. Each scene sample consists of (i) 3D laser range (LiDAR) data over a nearly spherical field of view, co-registered with (ii) spherical high dynamic range imagery, and (iii) a panorama of stereo image pairs. These data are publicly available at https://syns.soton.ac.uk/. I will discuss a number of challenges that we have addressed in the course of this project, including: 1) geographic sampling strategy, 2) scale selection for surface analysis, 3) relating scene measurements to human perception. I will also discuss future work and potential applications.

Meeting abstract presented at VSS 2016

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