论文标题
基于图像的食物能量估计深度域适应
Image Based Food Energy Estimation With Depth Domain Adaptation
论文作者
论文摘要
饮食摄入量的评估主要依赖于自我报告工具，这些仪器容易出现测量错误。饮食评估方法越来越多地纳入了技术进步，尤其是基于图像的方法，以解决这些局限性和进一步的自动化。基于图像的方法可以通过自动估算由移动设备捕获的图像来自动估算饮食摄入量来减轻用户负担和偏见。在本文中，我们提出了一个“能量密度图”，该图是从RGB图像到食物的能量密度的像素到像素映射。然后，我们将“能量密度图”与相关的深度图结合在一起，该图由深度传感器捕获以估计食物能量。在Nutrition5K数据集上评估了所提出的方法。实验结果表明，与基线方法相比，结果的改善，平均误差为13.29 kcal，平均地面真相和食品估计能量的平均百分比误差为13.57％。
Assessment of dietary intake has primarily relied on self-report instruments, which are prone to measurement errors. Dietary assessment methods have increasingly incorporated technological advances particularly mobile, image based approaches to address some of these limitations and further automation. Mobile, image-based methods can reduce user burden and bias by automatically estimating dietary intake from eating occasion images that are captured by mobile devices. In this paper, we propose an "Energy Density Map" which is a pixel-to-pixel mapping from the RGB image to the energy density of the food. We then incorporate the "Energy Density Map" with an associated depth map that is captured by a depth sensor to estimate the food energy. The proposed method is evaluated on the Nutrition5k dataset. Experimental results show improved results compared to baseline methods with an average error of 13.29 kCal and an average percentage error of 13.57% between the ground-truth and the estimated energy of the food.
