• DocumentCode
    1289306
  • Title

    Automatic generation of object recognition programs

  • Author

    Ikeuchi, Katsushi ; Kanade, Takeo

  • Author_Institution
    Dept. of Robotics Inst. & Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA, USA
  • Volume
    76
  • Issue
    8
  • fYear
    1988
  • fDate
    8/1/1988 12:00:00 AM
  • Firstpage
    1016
  • Lastpage
    1035
  • Abstract
    Issues and techniques are discussed to automatically compile object and sensor models into a visual recognition strategy for recognizing and locating an object in three-dimensional space from visual data. Automatic generation of recognition programs by compilation, in an attempt to automate this process, is described. An object model describes geometric and photometric properties of an object to be recognized. A sensor model specifies the sensor characteristics in predicting object appearances and variations of feature values. It is emphasized that the sensors, as well as objects, must be explicitly modeled to achieve the goal of automatic generation of reliable and efficient recognition programs. Actual creation of interpretation trees for two objects and their execution for recognition from a bin of parts are demonstrated
  • Keywords
    computer vision; pattern recognition; automatic generation of programs; compilation; computer vision; interpretation trees; object location; object model; object models; object recognition programs; photometric properties; predicting object appearances; recognition from bin of parts; sensor characteristics; sensor models; techniques; three-dimensional space; visual data; visual recognition strategy; Classification tree analysis; Data mining; Handwriting recognition; Machine vision; Object oriented modeling; Object recognition; Photometry; Predictive models; Sensor phenomena and characterization; Shape; Solid modeling; Target recognition;
  • fLanguage
    English
  • Journal_Title
    Proceedings of the IEEE
  • Publisher
    ieee
  • ISSN
    0018-9219
  • Type

    jour

  • DOI
    10.1109/5.5972
  • Filename
    5972