• DocumentCode
    342584
  • Title

    Combining geometric invariants with fuzzy clustering for object recognition

  • Author

    Walker, Ellen L.

  • Author_Institution
    Dept. of Math. Sci., Hiram Coll., OH, USA
  • fYear
    1999
  • fDate
    36342
  • Firstpage
    571
  • Lastpage
    574
  • Abstract
    Object recognition is the process of identifying the types and locations of objects in the image. Earlier work has shown the desirability of using fuzzy compatibility for local feature correspondence and fuzzy clustering for pose estimation of two dimensional objects. The paper extends the methodology to images of three dimensional objects by applying geometric invariants, specifically the cross ratio of four collinear points. The recognition process is divided into three subtasks: local feature correspondence, object identification, and pose determination. Algorithms are described for each subtask
  • Keywords
    computational geometry; fuzzy set theory; object recognition; pattern clustering; collinear points; cross ratio; fuzzy clustering; fuzzy compatibility; geometric invariants; local feature correspondence; object identification; object recognition; pose determination; pose estimation; recognition process; three dimensional objects; two dimensional objects; Cameras; Clustering algorithms; Computer vision; Educational institutions; Image databases; Image segmentation; Navigation; Object recognition; Robots; Shape;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Information Processing Society, 1999. NAFIPS. 18th International Conference of the North American
  • Conference_Location
    New York, NY
  • Print_ISBN
    0-7803-5211-4
  • Type

    conf

  • DOI
    10.1109/NAFIPS.1999.781758
  • Filename
    781758