Sparse Feature Detection

Sparse Feature Detection is a feature detection method that identifies a minimal set of salient, non-redundant features in a signal or image by exploiting sparsity constraints to prioritize isolated or infrequent patterns over dense or uniformly distributed responses.

85

Publications

5.7K

Citations

356

Authors

159

Institutions

Publications per year

2017–2026

39

Authors

356

  1. JY
    Jieping Ye

    Arizona State University, United States

    3 publications · 54 citations

    3

    h-index

  2. DW
    Damon Wing Kee Wong

    Singapore Eye Research Institute, Singapore

    2 publications · 47 citations

    2

    h-index

  3. CB
    Christoph Busch

    Darmstadt University of Applied Sciences, Germany

    2 publications · 57 citations

    2

    h-index

  4. JL
    Jiang Liu

    Institute for Infocomm Research, Singapore

    2 publications · 47 citations

    2

    h-index

  5. HA
    Haizhou Ai

    Tsinghua University, China

    2 publications · 427 citations

    2

    h-index

1–5 of 356

Venues

  1. 3

    h-index

  2. Neurocomputing

    3 publications · 98 citations

    3

    h-index

  3. arXiv (Cornell University)

    Repository · 3 publications · 62 citations

    3

    h-index

  4. Mathematical Problems in Engineering

    MPE · 2 publications · 24 citations

    2

    h-index

  5. The Annals of Statistics

    2 publications · 274 citations

    2

    h-index

1–5