A maximum-flow model for digital elastica shape optimization

DOI10.1007/978-3-030-76657-3_31zbMath1484.68270OpenAlexW3163544960WikidataQ125268915 ScholiaQ125268915MaRDI QIDQ2061833

Daniel Antunes, Hugues Talbot, Jacques-Olivier Lachaud

Publication date: 21 December 2021

Full work available at URL: https://doi.org/10.1007/978-3-030-76657-3_31

zbMATH Keywords

curvaturemultigrid convergencediscrete optimizationdigital estimators

Mathematics Subject Classification ID

Applications of mathematical programming (90C90) Computing methodologies for image processing (68U10) Computer graphics; computational geometry (digital and algorithmic aspects) (68U05)

Cites Work

• Nonlinear total variation based noise removal algorithms
• Flow by mean curvature of convex surfaces into spheres
• Euler's elastica and beyond
• Geodesic active contours
• An algorithm for total variation minimization and applications
• An elastica-driven digital curve evolution model for image segmentation
• Optimal approximations by piecewise smooth functions and associated variational problems
• Robust and Convergent Curvature and Normal Estimators with Digital Integral Invariants
• Unnamed Item

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