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Multi-Label Markov Random Fields as an Efficient and Effective Tool for Image Segmentation, Total Variations and Regularization

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Publication:2860122
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DOI10.4208/nmtma.2013.mssvm09zbMath1289.68201OpenAlexW4239030286WikidataQ128350043 ScholiaQ128350043MaRDI QIDQ2860122

Dorit S. Hochbaum

Publication date: 19 November 2013

Published in: Numerical Mathematics: Theory, Methods and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.4208/nmtma.2013.mssvm09


zbMATH Keywords

total variationimage segmentationMarkov random fieldsparametric cuts


Mathematics Subject Classification ID

Computing methodologies for image processing (68U10)


Related Items (6)

A unified approach for a 1D generalized total variation problem ⋮ Strong formulations for quadratic optimization with M-matrices and indicator variables ⋮ Applications and efficient algorithms for integer programming problems on monotone constraints ⋮ Unnamed Item ⋮ Total Variation Denoising in $l^1$ Anisotropy ⋮ Outlier Detection in Time Series via Mixed-Integer Conic Quadratic Optimization




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