Pith. sign in

REVIEW 1 cited by

From Active Contours to Minimal Geodesic Paths: New Solutions to Active Contours Problems by Eikonal Equations

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1907.09828 v2 pith:D2ZQW4VY submitted 2019-07-23 cs.CV

classification cs.CV
keywords minimalactivepathscontourseikonalgeodesicelasticaimage
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

In this chapter, we give an overview of part of our previous work based on the minimal path framework and the Eikonal partial differential equation (PDE). We show that by designing adequate Riemannian and Randers geodesic metrics the minimal paths can be utilized to search for solutions to almost all of the active contour problems and to the Euler-Mumford elastica problem, which allows to blend the advantages from minimal geodesic paths and those original approaches, i.e. the active contours and elastica curves. The proposed minimal path-based models can be applied to deal with a broad variety of image analysis tasks such as boundary detection, image segmentation and tubular structure extraction. The numerical implementations for the computation of minimal paths are known to be quite efficient thanks to the Eikonal solvers such as the Finsler variant of the fast marching method.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Equivariant Eikonal Neural Networks: Grid-Free, Scalable Travel-Time Prediction on Homogeneous Spaces

    cs.LG 2025-05 conditional novelty 6.0 of 10

    E-NES uses Lie-group point-cloud conditioning and equivariant neural fields to make grid-free eikonal travel-time prediction steerable under rotations and translations, with complete invariant features and competitive...

Pith tools