A comparison of food portion size estimation using geometric models and depth images
Six of the ten leading causes of death in the United States, including cancer, diabetes, and heart disease, can be directly linked to diet. Dietary intake, the process of determining what someone eats during the course of a day, provides valuable insights for mounting intervention programs for preve...
| Main Authors: | , , , , , |
|---|---|
| Format: | Conference Paper |
| Published: |
2016
|
| Online Access: | http://hdl.handle.net/20.500.11937/51080 |
| _version_ | 1848758609313267712 |
|---|---|
| author | Fang, S. Zhu, F. Jiang, C. Zhang, S. Boushey, Carol Delp, E. |
| author_facet | Fang, S. Zhu, F. Jiang, C. Zhang, S. Boushey, Carol Delp, E. |
| author_sort | Fang, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Six of the ten leading causes of death in the United States, including cancer, diabetes, and heart disease, can be directly linked to diet. Dietary intake, the process of determining what someone eats during the course of a day, provides valuable insights for mounting intervention programs for prevention of many of the above chronic diseases. Measuring accurate dietary intake is considered to be an open research problem in the nutrition and health fields. In this paper we compare two techniques of estimating food portion size from images of food. The techniques are based on 3D geometric models and depth images. An expectation-maximization based technique is developed to detect the reference plane in depth images, which is essential for portion size estimation using depth images. Our experimental results indicate that volume estimation based on geometric models is more accurate for objects with well-defined 3D shapes compared to estimation using depth images. |
| first_indexed | 2025-11-14T09:46:42Z |
| format | Conference Paper |
| id | curtin-20.500.11937-51080 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:46:42Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-510802017-09-13T15:49:10Z A comparison of food portion size estimation using geometric models and depth images Fang, S. Zhu, F. Jiang, C. Zhang, S. Boushey, Carol Delp, E. Six of the ten leading causes of death in the United States, including cancer, diabetes, and heart disease, can be directly linked to diet. Dietary intake, the process of determining what someone eats during the course of a day, provides valuable insights for mounting intervention programs for prevention of many of the above chronic diseases. Measuring accurate dietary intake is considered to be an open research problem in the nutrition and health fields. In this paper we compare two techniques of estimating food portion size from images of food. The techniques are based on 3D geometric models and depth images. An expectation-maximization based technique is developed to detect the reference plane in depth images, which is essential for portion size estimation using depth images. Our experimental results indicate that volume estimation based on geometric models is more accurate for objects with well-defined 3D shapes compared to estimation using depth images. 2016 Conference Paper http://hdl.handle.net/20.500.11937/51080 10.1109/ICIP.2016.7532312 restricted |
| spellingShingle | Fang, S. Zhu, F. Jiang, C. Zhang, S. Boushey, Carol Delp, E. A comparison of food portion size estimation using geometric models and depth images |
| title | A comparison of food portion size estimation using geometric models and depth images |
| title_full | A comparison of food portion size estimation using geometric models and depth images |
| title_fullStr | A comparison of food portion size estimation using geometric models and depth images |
| title_full_unstemmed | A comparison of food portion size estimation using geometric models and depth images |
| title_short | A comparison of food portion size estimation using geometric models and depth images |
| title_sort | comparison of food portion size estimation using geometric models and depth images |
| url | http://hdl.handle.net/20.500.11937/51080 |