Anisotropic Data Processing

Anisotropic Data Processing is a computational methodology that specifically addresses and leverages directional dependencies in data by applying non-uniform transformations or analyses to capture orientation-sensitive patterns that vary across spatial or feature dimensions.

36

Publications

1.4K

Citations

137

Authors

63

Institutions

Publications per year

2017–2026

6

Authors

137

  1. JA
    Jaakko Astola

    2 publications · 67 citations

    2

    h-index

  2. VK
    Vladimir Katkovnik

    2 publications · 67 citations

    2

    h-index

  3. KE
    Karen Egiazarian

    2 publications · 67 citations

    2

    h-index

  4. MK
    Michael Kazhdan

    Johns Hopkins University, United States

    2 publications · 43 citations

    2

    h-index

  5. MC
    Ming Chuang

    Johns Hopkins University, United States

    2 publications · 43 citations

    2

    h-index

1–5 of 137

Venues

  1. Computers & Geosciences

    CAGEO · 3 publications · 59 citations

    3

    h-index

  2. Geophysics

    3 publications · 67 citations

    3

    h-index

  3. Magnetic Resonance Imaging

    2 publications · 118 citations

    2

    h-index

  4. IEEE Transactions on Image Processing

    2 publications · 432 citations

    2

    h-index

  5. ACM Transactions on Graphics

    TOG · 1 publications · 20 citations

    1

    h-index

1–5