September 2011
Volume 11, Issue 11
Vision Sciences Society Annual Meeting Abstract  |   September 2011
Optimal disparity estimation in stereo-images of natural scenes
Author Affiliations
  • Johannes Burge
    Center for Perceptual Systems, University of Texas, Austin, USA
  • Wilson Geisler
    Center for Perceptual Systems, University of Texas, Austin, USA
Journal of Vision September 2011, Vol.11, 295. doi:
  • Views
  • Share
  • Tools
    • Alerts
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Johannes Burge, Wilson Geisler; Optimal disparity estimation in stereo-images of natural scenes. Journal of Vision 2011;11(11):295.

      Download citation file:

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

  • Supplements

Many animals, including humans, have substantial binocular overlap within their visual field. In the binocular zone, each eye's viewpoint yields a slightly different image of the same part of the scene. Binocular disparity – the local differences between the images – is a powerful cue for estimating the depth structure of the scene. But before disparity can be used for depth estimation, disparity must be estimated from the images. Psychophysical, neurophysiological, and computational studies have discovered many of the computational principles, cellular mechanisms, and behavioral limits of disparity estimation. However, methods for optimally estimating disparity in natural stereo-images given a vision system's constraints remain to be determined. Here, we describe a principled procedure for determining how to optimally estimate disparity given a set of natural stereo-images, an inter-ocular separation, a wave-optics model of each eye, and two photosensor arrays. First, we randomly selected a large set of patches from well-focused natural stereo-images; all had disparities within Panum's fusional range (+30 arcmin). Next, we passed the images through each eye's optics. Then, we removed undetectable image detail as predicted by the human retinal contrast detection threshold. Finally, we used a task-focused Bayesian statistical learning method to discover the spatial filters that are optimal for estimating disparity in natural stereo-images. We found the filters to be spatial frequency bandpass, with characteristics similar to disparity sensitive receptive fields in early visual cortex. We used the filters to obtain unbiased, high-precision estimates of disparity in 0.5 deg (or smaller) natural stereo-image patches. The optimal filters and estimation performance provide rigorous benchmarks against which existing behavioral, neurophysiological, and computational results can be evaluated.


This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.