• DocumentCode
    831558
  • Title

    Generic object recognition: building and matching coarse descriptions from line drawings

  • Author

    Bergevin, Robert ; Levine, Martin D.

  • Author_Institution
    Comput. Vision & Robotics Lab., McGill Univ., Montreal, Que., Canada
  • Volume
    15
  • Issue
    1
  • fYear
    1993
  • fDate
    1/1/1993 12:00:00 AM
  • Firstpage
    19
  • Lastpage
    36
  • Abstract
    Primal access recognition of visual objects (PARVO), a computer vision system that addresses the problem of fast and generic recognition of unexpected 3D objects from single 2D views, is considered. Recently, recognition by components (RBC), which is a new human image understanding theory, based on some psychological results, has been proposed as an explanation of how PARVO works. However, no systematic computational evaluation of its many aspects has yet been reported. The PARVO system discussed is a first step toward this goal, since its design respects and makes explicit the main assumptions of the proposed theory. It analyzes single-view 2D line drawings of 3D objects typical of the ones used in human image understanding studies. It is designed to handle partially occluded objects of different shape and dimension in various spatial orientations and locations in the image plane. The system is shown to successfully compute generic descriptions and then recognize many common man-made objects
  • Keywords
    artificial intelligence; computer vision; image recognition; 2D line drawings; PARVO; computer vision; generic object recognition; human image understanding; image recognition; primal access recognition of visual objects; recognition by components; spatial orientations; Computer vision; Humans; Image analysis; Intelligent robots; Labeling; Machine vision; Object recognition; Psychology; Shape; Visual system;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/34.184772
  • Filename
    184772