Four color theorem and convex relaxation for image segmentation with any number of regions

DOI10.3934/ipi.2013.7.1099zbMath1272.68450OpenAlexW1982495454WikidataQ57397262 ScholiaQ57397262MaRDI QIDQ371115

Xue-Cheng Tai, Ruiliang Zhang, Xavier Bresson, Tony F. Chan

Publication date: 27 September 2013

Published in: Inverse Problems and Imaging (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/ipi.2013.7.1099

zbMATH Keywords

Mumford-Shah modelconvex relaxation methodfour color theoremunsupervised segmentation

Mathematics Subject Classification ID

Computational aspects related to convexity (52B55) Numerical optimization and variational techniques (65K10) Computing methodologies for image processing (68U10)

Related Items (5)

A general framework of piecewise-polynomial Mumford–Shah model for image segmentation ⋮ Image segmentation based on the hybrid total variation model and the \(K\)-means clustering strategy ⋮ Variational image segmentation models involving non-smooth data-fidelity terms ⋮ Efficient global minimization methods for image segmentation models with four regions ⋮ Piecewise-smooth image segmentation models with \(L^1\) data-fidelity terms

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• RecPF

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