Advancements in Machine Learning and Computer Vision Approaches for Food and Nutrient Recognition from Images: A Survey
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8346-9_14
Reference34 articles.
1. Samad S, Ahmed F, Naher S, Kabir MA, Das A, Amin S, Islam SMS (2022) Smartphone apps for tracking food consumption and recommendations: evaluating artificial intelligence-based functionalities, features and quality of current apps. Intell Syst Appl 15:200103. https://doi.org/10.1016/j.iswa.2022.200103
2. Ma P, Lau CP, Yu N, Li A, Liu P, Wang Q, Sheng J (2021) Image-based nutrient estimation for Chinese dishes using deep learning. Food Res Int 147:110437. https://doi.org/10.1016/j.foodres.2021.110437
3. Ma P, Lau CP, Yu N, Li A, Sheng J (2022) Application of deep learning for image-based Chinese market food nutrients estimation. Food Chem 373:130994. https://doi.org/10.1016/j.foodchem.2021.130994
4. Fakhrou A, Kunhoth J, Al Maadeed S (2021) Smartphone-based food recognition system using multiple deep CNN models. Multimed Tools Appl 80:33011–33032. https://doi.org/10.1007/s11042-021-11329-6
5. Bossard L, Guillaumin M, Van Gool L (2014) Food-101—mining discriminative components with random forests. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (eds) Computer vision—ECCV 2014. Springer International Publishing, Cham, pp 446–461
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