Branch-and-mincut: global optimization for image segmentation with high-level priors
type: Item
instance of: scholarly article
title: Branch-and-mincut: global optimization for image segmentation with high-level priors
zbMATH Open document ID: 1255.68249
author: Victor Lempitsky
author: A. Blake
author: Carsten Rother
published in: Journal of Mathematical Imaging and Vision
publication date: 2013-01-22T00:00:00Z
Mathematics Subject Classification ID: 68U10
Mathematics Subject Classification ID: 90C26
zbMATH DE Number: 6128047
zbMATH Keywords: image segmentation
zbMATH Keywords: global optimization
zbMATH Keywords: shape priors
zbMATH Keywords: graph cuts
zbMATH Keywords: branch-and-bound
describes a project that uses: MAXFLOW
describes a project that uses: GrabCut
MaRDI profile type: Publication
full work available at URL: s10851-012-0328-0
OpenAlex ID: W1983084731
cites work: Pseudo-Boolean optimization
cites work: Completely convex formulation of the Chan-Vese image segmentation model
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cites work: A Fast Parametric Maximum Flow Algorithm and Applications
cites work: The Piecewise Smooth Mumford–Shah Functional on an Arbitrary Graph
cites work: Q4459498
cites work: Introduction to Information Retrieval
cites work: Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations
DOI: 10.1007/S10851-012-0328-0
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description: scientific article; zbMATH DE number 6128047