A survey and comparison of discrete and continuous multi-label optimization approaches for the Potts model

DOI10.1007/s11263-013-0619-yzbMath1304.68178OpenAlexW2113635243MaRDI QIDQ2254247

Daniel Cremers, Eno Töppe, Claudia Nieuwenhuis

Publication date: 4 February 2015

Published in: International Journal of Computer Vision (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11263-013-0619-y

zbMATH Keywords

surveyoptimizationcomparisonpartial differential equationsMarkov random fieldsmulti-label

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Computing methodologies for image processing (68U10) Pattern recognition, speech recognition (68T10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Machine vision and scene understanding (68T45)

Related Items (5)

Image segmentation based on the hybrid total variation model and the \(K\)-means clustering strategy ⋮ Segmentation of three-dimensional images with parametric active surfaces and topology changes ⋮ Sequential Convex Programming for Computing Information-Theoretic Minimal Partitions: Nonconvex Nonsmooth Optimization ⋮ Midrange geometric interactions for semantic segmentation. Constraints for continuous multi-label optimization ⋮ Semisupervised data classification via the Mumford-Shah-Potts-type model

Uses Software

• MAXFLOW

Cites Work

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