使用距离估计和深度学习在食物热量测量中进行重复校准
Using Distance Estimation and Deep Learning to Simplify Calibration in Food Calorie Measurement
摘要：
一方面，人体中的高卡路里摄入已被证明在许多情况下是有害的，导致多种疾病，另一方面，营养学家认为标准量的卡路里摄入对于维持人体中卡路里含量的正确平衡至关重要。因此，研究人员提出了各种自动工具和系统来帮助用户测量他们的卡路里摄入量。在本文中，我们考虑使用图像处理来识别食物的那些工具的类别，并且我们提出了一种用于全自动和用户友好的食物部分尺寸的尺寸的校准的方法，这是为了测量食物部分重量及其随后的卡路里量所需要的。实验结果表明，我们的方法，它使用深度学习，移动云计算，距离估计和尺寸校准内的移动终端，导致准确率提高到95%，平均比以前的工作
英文摘要：
High calorie intake in the human body on the one hand, has proved harmful in numerous occasions leading to several diseases and on the other hand, a standard amount of calorie intake has been deemed essential by dieticians to maintain the right balance of calorie content in human body. As such, researchers have proposed a variety of automatic tools and systems to assist users measure their calorie in-take. In this paper, we consider the category of those tools that use image processing to recognize the food, and we propose a method for fully automatic and user-friendly calibration of the dimension of the food portion sizes, which is needed in order to measure food portion weight and its ensuing amount of calories. Experimental results show that our method, which uses deep learning, mobile cloud computing, distance estimation and size calibration inside a mobile device, leads to an accuracy improvement to 95% on average compared to previous work
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
