Branch-and-mincut: global optimization for image segmentation with high-level priors

DOI10.1007/s10851-012-0328-0zbMath1255.68249OpenAlexW1983084731MaRDI QIDQ1932989

Carsten Rother, Andrew Blake, Victor Lempitsky

Publication date: 22 January 2013

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

Full work available at URL: https://doi.org/10.1007/s10851-012-0328-0

zbMATH Keywords

global optimizationbranch-and-boundimage segmentationgraph cutsshape priors

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26) Computing methodologies for image processing (68U10)

Related Items (8)

Globally optimal joint image segmentation and shape matching based on Wasserstein modes ⋮ Branch-and-mincut: global optimization for image segmentation with high-level priors ⋮ Inference and learning with hierarchical shape models ⋮ Generalized roof duality and bisubmodular functions ⋮ Variational surface reconstruction based on Delaunay triangulation and graph cut ⋮ Iterative graph cuts for image segmentation with a nonlinear statistical shape prior ⋮ Spatially constrained Student's \(t\)-distribution based mixture model for robust image segmentation ⋮ Simultaneous Convex Optimization of Regions and Region Parameters in Image Segmentation Models

Uses Software

• MAXFLOW
• GrabCut

Cites Work

• Unnamed Item
• Pseudo-Boolean optimization
• Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations
• Completely convex formulation of the Chan-Vese image segmentation model
• Branch-and-mincut: global optimization for image segmentation with high-level priors
• Introduction to Information Retrieval
• Active contours without edges
• A Fast Parametric Maximum Flow Algorithm and Applications
• The Piecewise Smooth Mumford–Shah Functional on an Arbitrary Graph

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