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A fast marching level set method for monotonically advancing fronts.

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Sethian, J A·1996·Proceedings of the National Academy of Sciences

Abstract

A fast marching level set method is presented for monotonically advancing fronts, which leads to an extremely fast scheme for solving the Eikonal equation. Level set methods are numerical techniques for computing the position of propagating fronts. They rely on an initial value partial differential equation for a propagating level set function and use techniques borrowed from hyperbolic conservation laws. Topological changes, corner and cusp development, and accurate determination of geometric properties such as curvature and normal direction are naturally obtained in this setting. This paper describes a particular case of such methods for interfaces whose speed depends only on local position. The technique works by coupling work on entropy conditions for interface motion, the theory of viscosity solutions for Hamilton-Jacobi equations, and fast adaptive narrow band level set methods. The technique is applicable to a variety of problems, including shape-from-shading problems, lithographic development calculations in microchip manufacturing, and arrival time problems in control theory.

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Metadata

Authors
Sethian, J. A.
Venue
Proceedings of the National Academy of Sciences
Published
1996-02-20
Volume
93
Issue
4
Pages
1591-1595
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W1999244633
Cited by
3,221
References
0
FWCI
17.45
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: A fast marching level set method for monotonically advancing fronts. https://app.cochat.ai/papers/10.1073/pnas.93.4.1591
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Sethian, J. A. (1996). A fast marching level set method for monotonically advancing fronts. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.93.4.1591

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.