Path-based distance with varying weights and neighborhood sequences

  • Authors:

  • Nicolas Normand;Robin Strand;Pierre Evenou;Aurore Arlicot

  • Affiliations:

  • IRCCyN UMR CNRS 6597, University of Nantes, France and School of Physics, Monash University, Melbourne, Australia;Centre for Image Analysis, Uppsala University, Sweden;IRCCyN UMR CNRS 6597, University of Nantes, France;IRCCyN UMR CNRS 6597, University of Nantes, France

  • Venue:
  • DGCI'11 Proceedings of the 16th IAPR international conference on Discrete geometry for computer imagery
  • Year:
  • 2011

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Abstract

This paper presents a path-based distance where local displacement costs vary both according to the displacement vector and with the travelled distance. The corresponding distance transform algorithm is similar in its form to classical propagation-based algorithms, but the more variable distance increments are either stored in look-uptables or computed on-the-fly. These distances and distance transform extend neighborhood-sequence distances, chamfer distances and generalized distances based on Minkowski sums. We introduce algorithms to compute, in Z2, a translated version of a neighborhood sequence distance map with a limited number of neighbors, both for periodic and aperiodic sequences. A method to recover the centered distance map from the translated one is also introduced. Overall, the distance transform can be computed with minimal delay, without the need to wait for the whole input image before beginning to provide the result image.