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Conference paper - Peer-reviewed, 2012

A Novel Algorithm for Computing Riemannian Geodesic Distance in Rectangular 2D Grids

Nilsson, Ola; Reimers, Martin; Museth, Ken; Brun, Anders

Abstract

We present a novel way to efficiently compute Riemannian geodesic distance over a two-dimensional domain. It is based on a previ- ously presented method for computation of geodesic distances on surface meshes. Our method is adapted for rectangular grids, equipped with a variable anisotropic metric tensor. Processing and visualization of such tensor fields is common in certain applications, for instance structure ten- sor fields in image analysis and diffusion tensor fields in medical imaging. The included benchmark study shows that our method provides signif- icantly better results in anisotropic regions and is faster than current stat-of-the-art solvers. Additionally, our method is straightforward to code; the test implementation is less than 150 lines of C++ code.

Published in

Lecture Notes in Computer Science
2012, Volume: 7432, pages: 265-274
Title: Advances in Visual Computing: 8th International Symposium, ISVC 2012, Rethymnon, Crete, Greece, July 16-18, 2012, Revised Selected Papers, Part II
ISBN: 978-3-642-33190-9, eISBN: 978-3-642-33191-6
Publisher: Springer Berlin Heidelberg

Conference

8th International Symposium on Visual Computing (ISVC), JUL 16-18, 2012, Rethymnon, GREECE

      SLU Authors

    • Brun, Anders

      • Centre for Image Analysis, Swedish University of Agricultural Sciences

    UKÄ Subject classification

    Computer Science

    Publication identifier

    DOI: https://doi.org/10.1007/978-3-642-33191-6_26

    Permanent link to this page (URI)

    https://res.slu.se/id/publ/43702