Automated Portion Estimation

Automated Portion Estimation is a computational method in dietary assessment that enables the quantification of food portion sizes from digital images or sensor data without manual input, distinguishing itself from traditional approaches by leveraging algorithms such as computer vision or machine learning to objectively and efficiently estimate serving amounts in nutritional intake analysis.

67

Publications

4.1K

Citations

260

Authors

96

Institutions

Publications per year

2017–2026

33

Authors

260

  1. CJ
    Carol J. Boushey

    University of Hawaiʻi at Mānoa, United States

    14 publications · 1K citations

    14

    h-index

  2. EJ
    Edward J. Delp

    Purdue University West Lafayette, United States

    11 publications · 872 citations

    11

    h-index

  3. FZ
    Fengqing Zhu

    Purdue University West Lafayette, United States

    8 publications · 749 citations

    8

    h-index

  4. MS
    Mingui Sun

    University of Pittsburgh, United States

    6 publications · 278 citations

    6

    h-index

  5. WJ
    Wenyan Jia

    University of Pittsburgh, United States

    5 publications · 217 citations

    5

    h-index

1–5 of 260

Venues

  1. 3

    h-index

  2. Journal of Diabetes Science and Technology

    DST · 3 publications · 298 citations

    3

    h-index

  3. Appetite

    3 publications · 65 citations

    3

    h-index

  4. Public Health Nutrition

    PNH · 2 publications · 147 citations

    2

    h-index

  5. 2

    h-index

1–5