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

Authors

48

  1. BS
    Bernhard Schölkopf

    Max Planck Institute for Intelligent Systems, Germany

    2 publications · 190 citations

    2

    h-index

  2. LZ
    Lumin Zhang

    Fudan University, China

    2 publications · 190 citations

    2

    h-index

  3. MK
    Martin Kiefel

    Max Planck Institute for Intelligent Systems, Germany

    2 publications · 190 citations

    2

    h-index

  4. CR
    Carsten Rother

    Microsoft Research (United Kingdom), United Kingdom

    2 publications · 190 citations

    2

    h-index

  5. PG
    Peter Gehler

    Max Planck Society, Germany

    2 publications · 190 citations

    2

    h-index

1–5 of 48

Venues

  1. Applied and Computational Harmonic Analysis

    1 publications · 15 citations

    1

    h-index

  2. IEEE Transactions on Image Processing

    1 publications · 39 citations

    1

    h-index

  3. Inverse Problems and Imaging

    1 publications · 33 citations

    1

    h-index

  4. Neural Information Processing Systems

    Conference · 1 publications · 87 citations

    1

    h-index

  5. Journal of Medical Imaging

    JMI · 1 publications · 19 citations

    1

    h-index

1–5