餐食
计算机科学
人工智能
卡路里
深度学习
计算机视觉
体积热力学
数据库
模式识别(心理学)
食品科学
医学
量子力学
物理
内分泌学
化学
作者
Ya Lu,Thomai Stathopoulou,Maria F. Vasiloglou,Lillian F. Pinault,Colleen Kiley,Elias K. Spanakis,Stavroula Mougiakakou
出处
期刊:Sensors
[MDPI AG]
日期:2020-07-31
卷期号:20 (15): 4283-4283
被引量:54
摘要
Accurate estimation of nutritional information may lead to healthier diets and better clinical outcomes. We propose a dietary assessment system based on artificial intelligence (AI), named goFOODTM. The system can estimate the calorie and macronutrient content of a meal, on the sole basis of food images captured by a smartphone. goFOODTM requires an input of two meal images or a short video. For conventional single-camera smartphones, the images must be captured from two different viewing angles; smartphones equipped with two rear cameras require only a single press of the shutter button. The deep neural networks are used to process the two images and implements food detection, segmentation and recognition, while a 3D reconstruction algorithm estimates the food’s volume. Each meal’s calorie and macronutrient content is calculated from the food category, volume and the nutrient database. goFOODTM supports 319 fine-grained food categories, and has been validated on two multimedia databases that contain non-standardized and fast food meals. The experimental results demonstrate that goFOODTM performed better than experienced dietitians on the non-standardized meal database, and was comparable to them on the fast food database. goFOODTM provides a simple and efficient solution to the end-user for dietary assessment.
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