• DocumentCode
    1425898
  • Title

    Similarity and symmetry measures for convex shapes using Minkowski addition

  • Author

    Heijmans, Henk J A M ; Tuzikov, Alexander V.

  • Author_Institution
    CWI, Amsterdam, Netherlands
  • Volume
    20
  • Issue
    9
  • fYear
    1998
  • fDate
    9/1/1998 12:00:00 AM
  • Firstpage
    980
  • Lastpage
    993
  • Abstract
    This paper is devoted to similarity and symmetry measures for convex shapes whose definition is based on Minkowski addition and the Brunn-Minkowski inequality. This means, in particular, that these measures are region-based, in contrast to most of the literature, where one considers contour-based measures. All measures considered in this paper are invariant under translations; furthermore, they can be chosen to be invariant under rotations, multiplications, reflections, or the class of affine transformations. It is shown that the mixed volume of a convex polygon and a rotation of another convex polygon over an angle θ is a piecewise concave function of θ. This and other results of a similar nature form the basis for the development of efficient algorithms for the computation of the given measures. Various results obtained in this paper are illustrated by experimental data. Although the paper deals exclusively with the two-dimensional case, many of the theoretical results carry over almost directly to higher-dimensional spaces
  • Keywords
    computational complexity; image processing; symmetry; Brunn-Minkowski inequality; Minkowski addition; convex polygon; convex shapes; efficient algorithms; mixed volume; multiplication invariance; piecewise concave function; reflection invariance; region-based measures; rotation invariance; similarity measures; symmetry measures; transformation invariance; translation invariance; Cameras; Distortion measurement; Helium; Humans; Machine vision; Particle measurements; Reflection; Reflectivity; Rotation measurement; Shape measurement;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/34.713363
  • Filename
    713363