• DocumentCode
    765327
  • Title

    Perceptual organization for scene segmentation and description

  • Author

    Mohan, Rakesh ; Nevatia, Ramakant

  • Author_Institution
    IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA
  • Volume
    14
  • Issue
    6
  • fYear
    1992
  • fDate
    6/1/1992 12:00:00 AM
  • Firstpage
    616
  • Lastpage
    635
  • Abstract
    A data-driven system for segmenting scenes into objects and their components is presented. This segmentation system generates hierarchies of features that correspond to structural elements such as boundaries and surfaces of objects. The technique is based on perceptual organization, implemented as a mechanism for exploiting geometrical regularities in the shapes of objects as projected on images. Edges are recursively grouped on geometrical relationships into a description hierarchy ranging from edges to the visible surfaces of objects. These edge groupings, which are termed collated features, are abstract descriptors encoding structural information. The geometrical relationships employed are quasi-invariant over 2-D projections and are common to structures of most objects. Thus, collations have a high likelihood of corresponding to parts of objects. Collations serve as intermediate and high-level features for various visual processes. Applications of collations to stereo correspondence, object-level segmentation, and shape description are illustrated
  • Keywords
    computerised pattern recognition; computerised picture processing; 2-D projections; abstract descriptors; boundaries; collated features; computerised pattern recognition; computerised picture processing; data-driven system; edge groupings; geometrical regularities; perceptual organization; scene segmentation; shape description; stereo correspondence; surfaces; Computer vision; Encoding; Feature extraction; Humans; Image segmentation; Intelligent robots; Layout; Machine vision; Shape; Stereo vision;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/34.141553
  • Filename
    141553