Image-based Hazard Classification

Image-Based Hazard Classification is a methodological framework within hazard classification that utilizes visual data as primary input to identify, categorize, and assess potential hazards through computational analysis of spatial, spectral, or temporal image features.

20

Publications

585

Citations

52

Authors

27

Institutions

Publications per year

2017–2026

14

Authors

52

  1. LS
    Lei Si

    University of Illinois at Springfield, United States

    1 publications · 16 citations

    1

    h-index

  2. MA
    M.Immanuel Adam

    Karlsruhe Institute of Technology, Germany

    1 publications · 16 citations

    1

    h-index

  3. ZW
    Zhongbin Wang

    China University of Mining and Technology, China

    1 publications · 16 citations

    1

    h-index

  4. LV
    Logesh Vasu

    1 publications · 14 citations

    1

    h-index

  5. AS
    Ahmed Shaker

    Toronto Metropolitan University, Canada

    1 publications · 10 citations

    1

    h-index

1–5 of 52

Venues

  1. Journal of Real-Time Image Processing

    1 publications · 55 citations

    1

    h-index

  2. Frontiers in Oncology

    1 publications · 27 citations

    1

    h-index

  3. Future Generation Computer Systems

    FGCS · 1 publications · 19 citations

    1

    h-index

  4. Journal of Applied Research and Technology

    1 publications · 10 citations

    1

    h-index

  5. Current Science

    1 publications · 16 citations

    1

    h-index

1–5