An Efficient Algorithm for Level Set Method Preserving Distance Function

Abstract

The level set method [31] is a popular technique for tracking moving interfaces in several disciplines including computer vision and fluid dynamics. However, despite its high edibility, the original level set method is limited by two important numerical issues. Firstly, the level set method does not implicitly preserve the level set function as a distance function, which is necessary to estimate accurately geometric features s.a. the curvature or the contour normal. Secondly, the level set algorithm is slow because the time step is limited by the standard CFL condition, which is also essential to the numerical stability of the iterative scheme. Recent advances with graph cut methods [4, 3] and continuous convex relaxation methods [7, 5, 16] provide powerful alternatives to the level set method for image processing problems because they are fast, accurate and guaranteed to nd the global minimizer independently to the initialization. These recent techniques use binary functions to represent the contour rather than distance functions, which are usually considered for the level set method.

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Document Details

Document Type
Technical Report
Publication Date
Sep 10, 2011
Accession Number
ADA557314

Entities

People

  • Dominique Zosso
  • Jean-philippe Thiran
  • Rongjie Lai
  • Stanley Osher
  • Virginia Estellers
  • Xavier Bresson

Organizations

  • University of California, Los Angeles

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Computational Science
  • Computer Graphics
  • Computer Science
  • Computer Vision
  • Computers
  • Electrical Engineering
  • Equations
  • Fluid Mechanics
  • Geometry
  • Image Processing
  • Image Recognition
  • Image Segmentation
  • Mathematics
  • Optimization
  • Personal Information Managers
  • Two Dimensional

Readers

  • Computer Vision.
  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Operations Research

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms