Minimal-delay distance transform for neighborhood-sequence distances in 2D and 3D

  • Authors:

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

  • Affiliations:

  • LUNAM Université, Université de Nantes, IRCCyN UMR CNRS 6597, Polytech Nantes, Rue Christian Pauc, La Chantrerie, 44306 Nantes Cedex 3, France;Centre for Image Analysis, Uppsala University, Sweden;LUNAM Université, Université de Nantes, IRCCyN UMR CNRS 6597, Polytech Nantes, Rue Christian Pauc, La Chantrerie, 44306 Nantes Cedex 3, France;LUNAM Université, Université de Nantes, IRCCyN UMR CNRS 6597, Polytech Nantes, Rue Christian Pauc, La Chantrerie, 44306 Nantes Cedex 3, France

  • Venue:
  • Computer Vision and Image Understanding
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

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-up-tables 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 a translated version of a neighborhood sequence distance map both for periodic and aperiodic sequences and a method to derive the centered distance map. A decomposition of the grid neighbors, in Z^2 and Z^3, allows to significantly decrease the number of displacement vectors needed for the distance transform. 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.