• DocumentCode
    3311969
  • Title

    Indexing visual representations through the complexity map

  • Author

    Dubuc, Benoit ; Zucker, Steven W.

  • Author_Institution
    McGill Res. Centre for Intelligent Machines, McGill Univ., Montreal, Que., Canada
  • fYear
    1995
  • fDate
    20-23 Jun 1995
  • Firstpage
    142
  • Lastpage
    149
  • Abstract
    In differential geometry curves are characterized as mappings from an interval to the plane. In topology curves are characterized as a Hausdorff space with certain countability properties. Neither of these definitions captures the role that curves play in vision, however, in which curves can denote simple objects (such as a straight line), or complicated objects (such as a jumble of string). The difference between these situations is in part a measure of their complexity, and in part a measure of their dimensionality. Note that the map defining such curves is unknown, as is the proper way to represent them. We propose a formal complexity theory of curves appropriate for computational vision in general, and for problems like separating straight lines from jumbles in particular. The theory is applied to the problem of perceptual grouping
  • Keywords
    computational complexity; computer vision; differential geometry; visual databases; Hausdorff space; complexity map; computational vision; countability properties; differential geometry; formal complexity theory; perceptual grouping; visual representations indexing; Area measurement; Complexity theory; Computer vision; Detectors; Geometry; Hair; Image edge detection; Indexing; Length measurement; Topology;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision, 1995. Proceedings., Fifth International Conference on
  • Conference_Location
    Cambridge, MA
  • Print_ISBN
    0-8186-7042-8
  • Type

    conf

  • DOI
    10.1109/ICCV.1995.466794
  • Filename
    466794