Name
Abstract | ||
|---|---|---|
A healthy diet is crucial for maintaining overall health and for controlling food-related chronic diseases, like diabetes and obesity. Proper diet management however, relies on the rather challenging task of food intake assessment and monitoring. To facilitate this procedure, several systems have been recently proposed for automatic meal assessment on mobile devices using computer vision methods. The development and validation of these systems requires large amounts of data and although some public datasets already exist, they don't cover the entire spectrum of inputs and/or uses. In this paper, we introduce a database, which contains RGB images of meals together with the corresponding depth maps, 3D models, segmentation and recognition maps, weights and volumes. We also present a number of experiments on the new database to provide baselines performances in the context of food segmentation, depth and volume estimation. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-70742-6_46 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Multimedia database,Pattern recognition,Segmentation,Computer science,Mobile device,Artificial intelligence,RGB color model,Volume estimation,Machine learning | Conference | 10590 |
ISSN | Citations | PageRank |
0302-9743 | 2 | 0.37 |
References | Authors | |
18 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dario Allegra | 1 | 56 | 12.89 |
| Marios Anthimopoulos | 2 | 247 | 13.75 |
| Joachim Dehais | 3 | 41 | 3.92 |
| Ya Lu | 4 | 2 | 2.74 |
| Filippo Stanco | 5 | 139 | 28.91 |
| Giovanni Maria Farinella | 6 | 412 | 57.13 |
| Stavroula G Mougiakakou | 7 | 342 | 28.61 |