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

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: 978-3-642-33191-6Publisher: 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