• DocumentCode
    990034
  • Title

    A new convexity measure for polygons

  • Author

    Zunic, Jovisa ; Rosin, Paul L.

  • Volume
    26
  • Issue
    7
  • fYear
    2004
  • fDate
    7/1/2004 12:00:00 AM
  • Firstpage
    923
  • Lastpage
    934
  • Abstract
    Convexity estimators are commonly used in the analysis of shape. In this paper, we define and evaluate a new convexity measure for planar regions bounded by polygons. The new convexity measure can be understood as a "boundary-based" measure and in accordance with this it is more sensitive to measured boundary defects than the so called "area-based" convexity measures. When compared with the convexity measure defined as the ratio between the Euclidean perimeter of the convex hull of the measured shape and the Euclidean perimeter of the measured shape then the new convexity measure also shows some advantages-particularly for shapes with holes. The new convexity measure has the following desirable properties: 1) the estimated convexity is always a number from (0, 1], 2) the estimated convexity is I if and only if the measured shape is convex, 3) there are shapes whose estimated convexity is arbitrarily close to 0, 4) the new convexity measure is invariant under similarity transformations, and 5) there is a simple and fast procedure for computing the new convexity measure.
  • Keywords
    computational geometry; set theory; shape measurement; Euclidean perimeter; area based convexity measure; boundary based convexity measure; boundary defects; computational geometry; convex hull; convexity estimators; planar regions; polygons; set theory; shape analysis; Area measurement; Biology computing; Indexing; Noise robustness; Powders; Shape measurement; Shape; convexity; measurement.; polygons; Algorithms; Artificial Intelligence; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/TPAMI.2004.19
  • Filename
    1300562