Level Set Methods

By Stanley J. Osher. Wiley Encyclopedia of Computer Science and Engineering, 2007

558 citations recorded by the source corpus.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/033bb6b05ebcd3583d73f007818b62e56caa0128

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In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

Citations in the corpus

Fast Global Minimization of the Active Contour/Snake Model (2007, methodology): “The PDE (3) is implemented with numerical schemes based on hyperbolic conservation laws, see Osher–Sethian [8–10], which can be highly accurate [11, 12] to give very fine/subpixel segmentations.”
Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction (2010, methodology): “One of the most simple and versatile schemes is the level set method of Osher and Sethian [36, 37, 41].”
Shape From Shading (2006, unknown): “More precisely, the algorithms of Rouy and Tourin [38], Dupuis and Oliensis [14], Sethian [39] and Prados and Faugeras [36, 34] can use Dirichlet and/or Soner conditions on the boundary of the image ∂ Ω at all the singular points S and on any other part of the image (for example, on an equal-height…”
Direct numerical simulations of gas/liquid multiphase flows (2011, methodology): “Other methods, based on similar ideas but advecting the marker function in a different way include the level set method (reviewed by Osher and Fedkiw, 2001; Sethian, 2001) and the CIP method of Yabe and collaborators (see Yabe et al., 2001, for a review).”
Manga colorization (2006, methodology): “The geometric level set method, proposed by Osher and Sethian, is a zero surface method [Osher and Sethian 1988; Sethian 1996; Sethian 1999].”
Continuous Global Optimization in Multiview 3D Reconstruction (2007, background): “A closer look at the evolution process reveals the difference to local optimization techniques like level sets (Sethian, 1996).”
Learning with Submodular Functions: A Convex Optimization Perspective (2011, background): “constant with respect to the graph, i.e., the constant sets are almost surely connected subsets of the graph. This property is the main reason for its wide-spread use in signal processing (see, e.g., [179, 37, 38]), machine learning and statistics [202]. For example, for a chain graph, the total var”