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From local maxima to connected skeletons.

C Arcelli1, L P Cordella, S Levialdi

  • 1Laboratorio di Cibernetica, C. N. R., Naples, Italy.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a parallel algorithm for generating connected skeletons from binary images. The method ensures skeleton connectivity, crucial for structural image analysis and image reconstruction.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Digital Image Analysis

Background:

  • Skeletonization is a key technique in digital image processing for creating simplified representations of objects.
  • Connectedness of the skeleton is important for structural image descriptions but not always guaranteed by existing methods.

Purpose of the Study:

  • To develop a parallel algorithm for generating connected skeletons from binary digital images.
  • To ensure the generated skeleton is connected and suitable for structural image analysis.
  • To enable the reconstruction of the original image from its skeleton.

Main Methods:

  • A parallel algorithm that propagates the background over the image step-by-step.
  • Selection of contour elements from significant convex regions or local maxima as skeleton elements.
  • Investigation and resolution of disconnections that may arise during skeletonization.

Main Results:

  • A novel parallel procedure for generating connected skeletons from binary images.
  • The algorithm ensures the resulting skeleton is a union of simple digital arcs.
  • Methods are provided to avoid disconnections, ensuring a robust skeleton.
  • The inclusion of all local maxima guarantees the possibility of image recovery via reverse distance transform.

Conclusions:

  • The proposed parallel algorithm effectively generates connected skeletons suitable for structural image analysis.
  • The method addresses the critical issue of skeleton connectivity, enhancing its utility.
  • The algorithm's ability to facilitate image reconstruction further validates its effectiveness.