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Abstract: These papers presents
new heuristially driven methods to quickly extract geodesic paths on images
and 3D meshes. We use a heuristic to drive the front propagation procedure
of the classical Fast Marching. This results in a modification of the
Fast Marching algorithm that is similar to the A* algorithm used in artificial
intelligence. In order to find very quickly geodesic paths between any
given couples of points, we advocate for the initial computation of distance
maps to a set of landmark points and make use of these distance maps through
a relevant heuristic. We show that our method brings a large speed up
for large scale applications that require the extraction of geodesics
on images and 3D meshes. We introduce two distortion metrics in order
to find an optimal seeding of landmark points for the targeted applications.
We also propose a compression scheme to reduce the memory requirement
without impacting the quality of the extracted paths.
Landmark-based Geodesic Computation for Heuristically Driven Path Planning
[PDF].
Gabriel Peyré
and Laurent Cohen,
Proceedings of CVPR'06,
June 2006.
Heuristically Driven Front Propagation for Geodesic Paths Extraction
[PDF].
Gabriel Peyré
and Laurent Cohen,
Proceedings of VLSM'05,
Springer
LNCS, p.173-184, Oct. 2005.
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