Name
Abstract | ||
|---|---|---|
The prevalence of diet-related chronic diseases strongly impacts global health and health services. Currently, it takes training and strong personal involvement to manage or treat these diseases. One way to assist with dietary assessment is through computer vision systems that can recognize foods and their portion sizes from images and output the corresponding nutritional information. When multiple food items may exist, a food segmentation stage should also be applied before recognition. In this study, we propose a method to detect and segment the food of already detected dishes in an image. The method combines region growing/merging techniques with a deep CNN-based food border detection. A semi-automatic version of the method is also presented that improves the result with minimal user input. The proposed methods are trained and tested on non-overlapping subsets of a food image database including 821 images, taken under challenging conditions and annotated manually. The automatic and semi-automatic dish segmentation methods reached average accuracies of 88% and 92%, respectively, in roughly 0.5 seconds per image. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2986035.2986047 | MADiMa @ ACM Multimedia |
Keywords | Field | DocType |
Diet assessment,dish segmentation,food recognition,diabetes,obesity,computer vision,smartphone | Portion Sizes,Computer vision,Segmentation,Computer science,Image segmentation,Region growing,Artificial intelligence,Image database,Health services,Merge (version control),Multimedia,Dietary assessment | Conference |
Citations | PageRank | References |
4 | 0.41 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joachim Dehais | 1 | 41 | 3.92 |
| Marios Anthimopoulos | 2 | 247 | 13.75 |
| Stavroula G Mougiakakou | 3 | 342 | 28.61 |