Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
  1. Home
  2. Search

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

3 results

  • Reliable disparity estimation through selective integration

  • MICHAEL S. GRAY, ALEXANDRE POUGET, RICHARD S. ZEMEL, STEVEN J. NOWLAN, TERRENCE J. SEJNOWSKI
  • Journal:
    Visual Neuroscience / Volume 15 / Issue 3 / March 1998
    Published online by Cambridge University Press:
    01 March 1998, pp. 511-528

  • A network model of disparity estimation was developed based on disparity-selective neurons, such as those found in the early stages of processing in the visual cortex. The model accurately estimated multiple disparities in regions, which may be caused by transparency or occlusion. The selective integration of reliable local estimates enabled the network to generate accurate disparity estimates on normal and transparent random-dot stereograms. The model was consistent with human psychophysical results on the effects of spatial-frequency filtering on disparity sensitivity. The responses of neurons in macaque area V2 to random-dot stereograms are consistent with the prediction of the model that a subset of neurons responsible for disparity selection should be sensitive to disparity gradients.