Motion Denoising

Motion Denoising is a computational method in motion capture that refines noisy motion data by suppressing artifacts and preserving essential kinematic features through signal processing or machine learning techniques.

93

Publications

4.5K

Citations

328

Authors

167

Institutions

Publications per year

2017–2026

49

Authors

328

  1. OC
    Oscar C. Au

    Hong Kong University of Science and Technology, Hong Kong SAR China

    3 publications · 81 citations

    3

    h-index

  2. PB
    Patrick Bouthémy

    Institut de Recherche en Informatique et Systèmes Aléatoires, France

    3 publications · 328 citations

    3

    h-index

  3. WP
    Wilfried Philips

    Ghent University, Belgium

    3 publications · 109 citations

    3

    h-index

  4. JZ
    Jianjun Zhang

    Bournemouth University, United Kingdom

    2 publications · 94 citations

    2

    h-index

  5. YF
    Yinfu Feng

    Zhejiang University, China

    2 publications · 94 citations

    2

    h-index

1–5 of 328

Venues

  1. 4

    h-index

  2. arXiv (Cornell University)

    Repository · 4 publications · 95 citations

    4

    h-index

  3. 3

    h-index

  4. NeuroImage

    3 publications · 182 citations

    3

    h-index

  5. 3

    h-index

1–5