Summary of How Much You Ate? Food Portion Estimation on Spoons, by Aaryam Sharma et al.
How Much You Ate? Food Portion Estimation on Spoons
by Aaryam Sharma, Chris Czarnecki, Yuhao Chen, Pengcheng Xi, Linlin Xu, Alexander Wong
First submitted to arxiv on: 12 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents an innovative solution for monitoring dietary intake through food portion estimation using stationary cameras on utensils. Current state-of-the-art image-based algorithms have limitations, such as requiring users to take images of their meals from a top-down perspective, which can be inconvenient and inaccurate. The proposed system utilizes the shallow depth of utensils to capture food items more accurately without the need for post-meal image capture. This method is reliable for estimating nutritional content in liquid-solid heterogeneous mixtures like soups and stews. Through experiments, the authors demonstrate the potential of this approach as a non-invasive, user-friendly, and highly accurate dietary intake monitoring tool. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to make it easier to track what we eat by using cameras on utensils. Right now, people have to take pictures of their food from above, which can be tricky and not very accurate. The new system takes photos as you eat, without needing to change camera angles or take extra pictures after meals. It’s great for monitoring liquid-solid foods like soups and stews. The authors tested this method and found it to be an effective way to track dietary intake. |
