Topological Feature Extraction

Topological Feature Extraction is a computational methodology that identifies and quantifies invariant structural properties of data across different scales by leveraging tools from algebraic topology, particularly persistent homology, to reveal qualitative geometric patterns such as connected components, loops, and voids that persist under continuous deformations.

284

Publications

19.4K

Citations

869

Authors

411

Institutions

Publications per year

2017–2026

143

Authors

869

  1. VP
    Valerio Pascucci

    Lawrence Livermore National Laboratory, United States

    8 publications · 718 citations

    8

    h-index

  2. AG
    Attila Gyulassy

    University of Utah, United States

    8 publications · 592 citations

    8

    h-index

  3. YH
    Yasuaki Hiraoka

    Tohoku University, Japan

    6 publications · 350 citations

    6

    h-index

  4. BH
    Bernd Hamann

    University of California, Davis, United States

    5 publications · 441 citations

    5

    h-index

  5. VN
    Vijay Natarajan

    Indian Institute of Science Bangalore, India

    5 publications · 441 citations

    5

    h-index

1–5 of 869

Venues

  1. arXiv (Cornell University)

    Repository · 13 publications · 504 citations

    11

    h-index

  2. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 7 publications · 518 citations

    7

    h-index

  3. Computer Vision Graphics and Image Processing

    4 publications · 3.4K citations

    4

    h-index

  4. Computer Graphics Forum

    4 publications · 172 citations

    4

    h-index

  5. Physical review. B./Physical review. B

    PRB · 4 publications · 200 citations

    4

    h-index

1–5