Fast Euclidean distance transformation in two scans using a 3 × 3 neighborhood

  • Authors:
  • Frank Y. Shih;Yi-Ta Wu

  • Affiliations:
  • Computer Vision Laboratory, College of Computing Sciences, New Jersey Institute of Technology, Newark, NJ;Computer Vision Laboratory, College of Computing Sciences, New Jersey Institute of Technology, Newark, NJ

  • Venue:
  • Computer Vision and Image Understanding
  • Year:
  • 2004

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Abstract

Cuisenaire and Macq [Comp. Vis. Image Understand., 76(2) (1999) 163] proposed a fast Euclidean distance transformation (EDT) by propagation using multiple neighborhoods and bucket sorting. To save the time for bucket sorting and to reduce the complexity of multiple neighborhoods, we propose a new, simple and fast EDT in two scans using a 3 × 3 neighborhood. By recording the relative x- and y-coordinates, an optimal two-scan algorithm can be developed to achieve the EDT correctly and efficiently in a constant time without iterations.