Conditional Random Field Segmentation

Conditional Random Field Segmentation is a probabilistic graphical model-based image segmentation method that employs conditional random fields to model the dependencies between pixel labels and observed image features, enabling structured prediction by integrating local evidence with global contextual coherence.

277

Publications

17.2K

Citations

1K

Authors

407

Institutions

Publications per year

2017–2026

149

Authors

1K

  1. EK
    Evangelos Kalogerakis

    University of Massachusetts Amherst, United States

    5 publications · 1K citations

    5

    h-index

  2. AS
    Ahmed Soliman

    University of Louisville, United States

    3 publications · 53 citations

    3

    h-index

  3. DL
    Douglas L. Arnold

    Montreal Neurological Institute and Hospital, Canada

    3 publications · 88 citations

    3

    h-index

  4. CK
    Chaitanya Kolluru

    Case Western Reserve University, United States

    3 publications · 134 citations

    3

    h-index

  5. PZ
    Peng Zhang

    Xidian University, China

    3 publications · 124 citations

    3

    h-index

1–5 of 1K

Venues

  1. IEEE Access

    12 publications · 826 citations

    12

    h-index

  2. arXiv (Cornell University)

    Repository · 14 publications · 390 citations

    10

    h-index

  3. IEEE Transactions on Medical Imaging

    10 publications · 1K citations

    10

    h-index

  4. Remote Sensing

    8 publications · 281 citations

    8

    h-index

  5. Sensors

    5 publications · 216 citations

    5

    h-index

1–5