Pattern Recognition and Image Analysis

, Volume 19, Issue 2, pp 277–283 | Cite as

Pearling: Stroke segmentation with crusted pearl strings

  • B. WhitedEmail author
  • J. Rossignac
  • G. Slabaugh
  • T. Fang
  • G. Unal
Application Problems


We introduce a novel segmentation technique, called Pearling, for the semi-automatic extraction of idealized models of networks of strokes (variable width curves) in images. These networks may for example represent roads in an aerial photograph, vessels in a medical scan, or strokes in a drawing. The operator seeds the process by selecting representative areas of good (stroke interior) and bad colors. Then, the operator may either provide a rough trace through a particular path in the stroke graph or simply pick a starting point (seed) on a stroke and a direction of growth. Pearling computes in realtime the centerlines of the strokes, the bifurcations, and the thickness function along each stroke, hence producing a purified medial axis transform of a desired portion of the stroke graph. No prior segmentation or thresholding is required. Simple gestures may be used to trim or extend the selection or to add branches. The realtime performance and reliability of Pearling results from a novel disk-sampling approach, which traces the strokes by optimizing the positions and radii of a discrete series of disks (pearls) along the stroke. A continuous model is defined through subdivision. By design, the idealized pearl string model is slightly wider than necessary to ensure that it contains the stroke boundary. A narrower core model that fits inside the stroke is computed simultaneously. The difference between the pearl string and its core contains the boundary of the stroke and may be used to capture, compress, visualize, or analyze the raw image data along the stroke boundary.


Medial Axis Discrete Series Gabor Wavelet Georgia Tech Vessel Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Pleiades Publishing, Ltd. 2009

Authors and Affiliations

  • B. Whited
    • 1
    Email author
  • J. Rossignac
    • 1
  • G. Slabaugh
    • 2
  • T. Fang
    • 2
  • G. Unal
    • 2
  1. 1.Georgia Institute of Technology, Graphics, Visualization and Usability CenterAtlantaUSA
  2. 2.Intelligent Vision and Reasoning DepartmentSiemens Corporate ResearchPrincetonUSA

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