Efficient global minimization methods for image segmentation models with four regions

DOI10.1007/s10851-014-0507-2zbMath1331.68263OpenAlexW2064785575MaRDI QIDQ2353417

Xue-Cheng Tai, Egil Bae

Publication date: 14 July 2015

Published in: Journal of Mathematical Imaging and Vision (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10851-014-0507-2

zbMATH Keywords

global optimizationconvex optimizationcombinatorial optimizationimage segmentationvariational models

Mathematics Subject Classification ID

Numerical optimization and variational techniques (65K10) Computing methodologies for image processing (68U10)

Related Items (max. 100)

An Active Contour Model with Local Variance Force Term and Its Efficient Minimization Solver for Multiphase Image Segmentation ⋮ Convex variational methods on graphs for multiclass segmentation of high-dimensional data and point clouds ⋮ A Three-Stage Variational Image Segmentation Framework Incorporating Intensity Inhomogeneity Information ⋮ A multigrid algorithm for maxflow and min-cut problems with applications to multiphase image segmentation ⋮ Two-Phase Segmentation for Intensity Inhomogeneous Images by the Allen--Cahn Local Binary Fitting Model

Uses Software

• MAXFLOW

Cites Work

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• Four color theorem and convex relaxation for image segmentation with any number of regions
• Global minimization for continuous multiphase partitioning problems using a dual approach
• Natural discretizations for the divergence, gradient, and curl on logically rectangular grids
• An algorithm for total variation minimization and applications
• A multiphase level set framework for image segmentation using the Mumford and Shah model
• Completely convex formulation of the Chan-Vese image segmentation model
• Combinatorial optimization of the discretized multiphase Mumford-Shah functional
• Image segmentation by iterative optimization of multiphase multiple piecewise constant model and Four-Color relabeling
• A spatially continuous max-flow and min-cut framework for binary labeling problems
• Multiplier and gradient methods
• Tight Convex Relaxations for Vector-Valued Labeling
• Optimal approximations by piecewise smooth functions and associated variational problems
• Global Solutions of Variational Models with Convex Regularization
• Algorithms for Finding Global Minimizers of Image Segmentation and Denoising Models
• Lagrange Multiplier Approach to Variational Problems and Applications
• A binary level set model and some applications to Mumford-Shah image segmentation
• Graph Cut Optimization for the Piecewise Constant Level Set Method Applied to Multiphase Image Segmentation
• Maximal flow through a domain
• Roof duality, complementation and persistency in quadratic 0–1 optimization
• Active contours without edges
• A Convex Approach to Minimal Partitions
• Scale Space and PDE Methods in Computer Vision

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