A Cross-Modal Variational Framework For Food Image Analysis
T. Theodoridis, V. Solachidis, K. Dimitropoulso, and P. Daras
Food analysis resides at the core of modern nutrition recommender systems, providing the foundation for a high-level understanding of users’ eating habits…. https://ieeexplore.ieee.org/document/9190758
Thomas Theodoridis, Vassilios Solachidis, Kosmas Dimitropoulos, Lazaros Gymnopoulos, and Petros Daras
The goal of this work is to provide an overview of existing approaches regarding AI nutrition recommender systems. A breakdown of such systems into task-specific components is presented, as well as methodologies concerned with each individual….
A Deep Network for Automatic Video-Based Food Bite Detection
Konstantinidis D., Dimitropoulos K., Ioakimidis I., Langlet B., Daras P. (2019) A Deep Network for Automatic Video-Based Food Bite Detection. In: Tzovaras D., Giakoumis D., Vincze M., Argyros A.
Past research has now provided compelling evidence pointing towards correlations among individual eating styles and the development of (un)healthy eating patterns, obesity and other medical conditions. In this setting, an automatic, non-invasive…
Single Image-Based Food Volume Estimation Using Monocular Depth-Prediction Networks
Graikos A., Charisis V., Iakovakis D., Hadjidimitriou S., Hadjileontiadis L. (2020) Single Image-Based Food Volume Estimation Using Monocular Depth-Prediction Networks. In: Antona M., Stephanidis C.
In this work, we present a system that can estimate food volume from a single input image, by utilizing the latest advancements in monocular depth estimation. We employ a state-of-the-art, monocular depth prediction network architecture…