• DocumentCode
    3862081
  • Title

    Piecewise linear skeletonization using principal curves

  • Author

    B. Kegl;A. Krzyzak

  • Author_Institution
    Dept. of Comput. Sci. & Operational Res., Montreal Univ., Que., Canada
  • Volume
    24
  • Issue
    1
  • fYear
    2002
  • Firstpage
    59
  • Lastpage
    74
  • Abstract
    Proposes an algorithm to find piecewise linear skeletons of handwritten characters by using principal curves. The development of the method was inspired by the apparent similarity between the definition of principal curves (smooth curves which pass through the "middle" of a cloud of points) and medial axes (smooth curves that run equidistantly from the contours of a character image). The central fitting-and-smoothing step of the algorithm is an extension of the polygonal line algorithm, which approximates principal curves of data sets by piecewise linear curves. The polygonal line algorithm is extended to find principal graphs and complemented with two steps specific to the task of skeletonization: an initialization method to capture the approximate topology of the character, and a collection of restructuring operations to improve the structural quality of the skeleton produced by the initialization method. An advantage of our approach over existing methods is that we optimize the skeleton graph by minimizing an intuitive and explicit objective function that captures the two competing criteria of smoothing the skeleton and fitting it closely to the pixels of the character image. We tested the algorithm on isolated handwritten digits and images of continuous handwriting. The results indicated that the proposed algorithm can find a smooth medial axis in the great majority of a wide variety of character templates and that it substantially improves the pixel-wise skeleton obtained by traditional thinning methods.
  • Keywords
    "Piecewise linear techniques","Skeleton","Pixel","Clouds","Piecewise linear approximation","Image storage","Topology","Optimization methods","Smoothing methods","Testing"
  • Journal_Title
    IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/34.982884
  • Filename
    982884