On total variation minimization and surface evolution using parametric maximum flows
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Publication:847514
DOI10.1007/s11263-009-0238-9zbMath1371.94073OpenAlexW1986753844MaRDI QIDQ847514
Jérôme Darbon, Antonin Chambolle
Publication date: 16 February 2010
Published in: International Journal of Computer Vision (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11263-009-0238-9
total variationvariational approachessubmodular functionsmax-flow/min-cutcrystalline and anisotropic mean curvature flowparametric max-flow algorithms
Variational methods applied to PDEs (35A15) Variational problems in a geometric measure-theoretic setting (49Q20) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
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Cites Work
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- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
- Pseudo-Boolean optimization
- Crystalline mean curvature flow of convex sets
- Complexity and algorithms for convex network optimization and other nonlinear problems
- Implicit time discretization of the mean curvature flow with a discontinuous forcing term
- On submodular function minimization
- The ellipsoid method and its consequences in combinatorial optimization
- Facet-breaking for three-dimensional crystals evolving by mean curvature
- Implicit time discretization for the mean curvature flow equation
- Anisotropic motion by mean curvature in the context of Finsler geometry
- Introductory lectures on convex optimization. A basic course.
- A characterization of convex calibrable sets in \(\mathbb R^N\)
- Variational algorithms and pattern formation in dendritic solidification
- A combinatorial algorithm minimizing submodular functions in strongly polynomial time.
- An algorithm for mean curvature motion
- Image restoration with discrete constrained total variation. I: Fast and exact optimization
- Anisotropic curvature-driven flow of convex sets
- On active contour models and balloons
- Parametric Maximum Flow Algorithms for Fast Total Variation Minimization
- A combinatorial, strongly polynomial-time algorithm for minimizing submodular functions
- Total Variation Regularization for Image Denoising, I. Geometric Theory
- The Discontinuity Set of Solutions of the TV Denoising Problem and Some Extensions
- A Viscosity Solutions Approach to Shape-From-Shading
- Minimum cuts and related problems
- Mathematical Techniques for Efficient Record Segmentation in Large Shared Databases
- Fast Marching Methods
- Curvature-Driven Flows: A Variational Approach
- A Fast Parametric Maximum Flow Algorithm and Applications
- Efficient algorithms for globally optimal trajectories
- APPROXIMATION OF THE ANISOTROPIC MEAN CURVATURE FLOW
- Aspects of Total Variation RegularizedL1Function Approximation
- An efficient algorithm for image segmentation, Markov random fields and related problems
- Signal Recovery by Proximal Forward-Backward Splitting