Random Field Regularization
Random Field Regularization is a mathematical framework that imposes structural constraints on random field models by incorporating prior knowledge or smoothness assumptions to stabilize inference and reduce uncertainty in spatial or spatiotemporal predictions.
13
Publications
567
Citations
48
Authors
30
Institutions
Publications per year
2017–2026
2
Parents
13
Structure learning in random fields for heart motion abnormality detection
Mark Schmidt, Kevin Murphy, Glenn Fung et al. · 2008 · 111 citations
Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance
Peter Gehler, Carsten Rother, Martin Kiefel et al. · 2011 · 103 citations
Scene Intrinsics and Depth from a Single Image
Evan Shelhamer, Jonathan T. Barron, Trevor Darrell · 2015 · 52 citations
Sverrir Guðmundsson, Magnús T. Guðmundsson, Helgi Björnsson et al. · Annals of Glaciology · 2002 · 43 citations · Full text
1–5 of 13
48
Max Planck Institute for Intelligent Systems, Germany
2 publications · 190 citations
2
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2
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2
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2
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2
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1–5 of 48
30
Max Planck Society
Munich, Germany
2 h-index
9 publications
847 citations
Max Planck Institute for Informatics
Saarbrücken, Germany
2 h-index
3 publications
277 citations

Centre National de la Recherche Scientifique
Paris, France
2 h-index
2 publications
49 citations
University of California, Berkeley
Berkeley, United States
1 h-index
2 publications
104 citations
Université Paris Cité
Paris, France
1 h-index
1 publications
10 citations

La Trobe University
Melbourne, Australia
1 h-index
1 publications
15 citations

University of Reggio Calabria
Reggio Calabria, Italy
1 h-index
1 publications
13 citations
Institut Pierre-Simon Laplace
Paris, France
1 h-index
2 publications
78 citations
1–8 of 30
1 publications · 15 citations
1
h-index
1 publications · 39 citations
1
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1 publications · 33 citations
1
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Conference · 1 publications · 87 citations
1
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JMI · 1 publications · 19 citations
1
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1–5