• DocumentCode
    847209
  • Title

    Cepstral filtering on a columnar image architecture: a fast algorithm for binocular stereo segmentation

  • Author

    Yeshurun, Yehezkel ; Schwartz, Eric L.

  • Author_Institution
    Dept. of Psychiatry, New York Univ. Med. Center, NY, USA
  • Volume
    11
  • Issue
    7
  • fYear
    1989
  • fDate
    7/1/1989 12:00:00 AM
  • Firstpage
    759
  • Lastpage
    767
  • Abstract
    Many primate visual cortex architectures have a prominent feature responsible for the mixing of left and right eye visual data: ocular dominance columns represent thin (about 5-10 minutes of arc) strips of alternating left and right eye input to the brain. It is shown that such an architecture, when operated upon with a cepstral filter, provides a strong cue for binocular stereopsis. Specifically, the vector of binocular disparity may be easily identified in the output of the (columnar based) cepstral filter. This algorithm is illustrated with application to a random dot stereogram and to natural images. The authors suggest that this provides a fast algorithm for stereo segmentation, in a machine vision context. In a biological context, it may provide a computational rationale for the existence of columnar systems with regard to both ocular mixing and other visual modalities that have a columnar architecture
  • Keywords
    computer vision; filtering and prediction theory; pattern recognition; visual perception; binocular disparity; binocular stereo segmentation; binocular stereopsis; brain; cepstral filter; columnar image architecture; computer vision; machine vision; natural images; ocular dominance columns; pattern recognition; primate visual cortex architectures; random dot stereogram; stereo segmentation; Biomedical imaging; Cepstral analysis; Filtering algorithms; Filters; Humans; Image segmentation; Laboratories; Layout; Machine vision; Psychiatry;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/34.192471
  • Filename
    192471