用图像估算食物分量，突破二维到三维的难题
Food Portion Estimation: From Pixels to Calories
- 结合单图、辅助输入或模板匹配等策略提升估算精度
- 利用深度学习从2D图像精准推断食物三维体积
- 适合营养评估与慢性病管理领域的研究者参考
基于图像的饮食评估是准确便捷监测个人健康的重要策略，在慢性病和肥胖预防与管理中具有关键作用。然而，图像法在从二维图像估计食物三维尺寸方面仍面临挑战。已有多种方法尝试克服这一局限，如使用深度图、多视角输入或基于模型的模板匹配等。深度学习进一步推动了该领域进展，可通过单目图像或图像与辅助信息的组合，精确预测食物分量。本文系统探讨了实现高精度食物分量估算的不同策略。
原文摘要 · Abstract (English)
Reliance on images for dietary assessment is an important strategy to accurately and conveniently monitor an individual's health, making it a vital mechanism in the prevention and care of chronic diseases and obesity. However, image-based dietary assessment suffers from estimating the three dimensional size of food from 2D image inputs. Many strategies have been devised to overcome this critical limitation such as the use of auxiliary inputs like depth maps, multi-view inputs, or model-based approaches such as template matching. Deep learning also helps bridge the gap by either using monocular images or combinations of the image and the auxillary inputs to precisely predict the output portion from the image input. In this paper, we explore the different strategies employed for accurate portion estimation.
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