Classical Optical Flow

Classical Optical Flow is a foundational methodology in computer vision that estimates pixel-wise motion between consecutive image frames by solving the optical flow constraint equation under assumptions of brightness constancy and spatial smoothness.

41

Publications

5.8K

Citations

105

Authors

53

Institutions

Publications per year

2017–2026

11

Authors

105

  1. MJ
    Michael J. Black

    Max Planck Institute for Intelligent Systems, Germany

    4 publications · 3.4K citations

    4

    h-index

  2. JL
    Jerry L. Prince

    Johns Hopkins University, United States

    3 publications · 100 citations

    3

    h-index

  3. VK
    Vladlen Koltun

    Intel (United States), United States

    2 publications · 184 citations

    2

    h-index

  4. QC
    Qifeng Chen

    Hong Kong University of Science and Technology, Hong Kong SAR China

    2 publications · 184 citations

    2

    h-index

  5. SR
    Stefan Roth

    Technische Universität Darmstadt, Germany

    2 publications · 2K citations

    2

    h-index

1–5 of 105

Venues

  1. Experiments in Fluids

    3 publications · 271 citations

    3

    h-index

  2. Vision Research

    2 publications · 105 citations

    2

    h-index

  3. International Journal of Computer Vision

    2 publications · 736 citations

    2

    h-index

  4. IEEE Transactions on Image Processing

    2 publications · 238 citations

    2

    h-index

  5. Computer Vision and Image Understanding

    CVIU · 2 publications · 63 citations

    2

    h-index

1–5