Shape Complexity Metrics

Shape Complexity Metrics is a computational metric that quantifies the intricacy of geometric forms in edge-based representations by analyzing structural features such as curvature, branching patterns, and topological irregularities inherent to the shape's轮廓.

102

Publications

23.8K

Citations

310

Authors

144

Institutions

Publications per year

2017–2026

22

Authors

310

  1. JG
    James Gardiner

    University of Liverpool, United Kingdom

    4 publications · 176 citations

    4

    h-index

  2. CB
    Charlotte Brassey

    University of Manchester, United Kingdom

    4 publications · 176 citations

    4

    h-index

  3. DP
    David Page

    University of Tennessee at Knoxville, United States

    3 publications · 158 citations

    3

    h-index

  4. YM
    Yoshiyuki Matsuoka

    Keio University, Japan

    3 publications · 66 citations

    3

    h-index

  5. SR
    Sreenivas R. Sukumar

    Oak Ridge National Laboratory, United States

    3 publications · 158 citations

    3

    h-index

1–5 of 310

Venues

  1. PLoS ONE

    4 publications · 482 citations

    4

    h-index

  2. International Journal of Remote Sensing

    3 publications · 68 citations

    3

    h-index

  3. Journal of Applied Clinical Medical Physics

    JACMP · 2 publications · 89 citations

    2

    h-index

  4. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 2 publications · 18.7K citations

    2

    h-index

  5. Physical Review Letters

    PRL · 2 publications · 186 citations

    2

    h-index

1–5