盘子提取法结合神经网络用于医学上的摄食量测量系统

F. Takeda, K. Kumada, M. Takara
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引用次数: 26

摘要

我们一直致力于开发一种食物摄入测量系统。这个系统测量托盘上每道菜的食物摄入量。因此,本系统需要从托盘图像中提取盘子图像。本文提出了一种基于神经网络的菜肴提取方法。我们期望所提出的方法能够高效准确地提取出盘片图像,即使盘片相互过度包裹。针对食物有时会导致提取的菜肴图像无法正确识别的问题,我们在菜肴提取方法中增加了食物拒绝算法。最后,通过实际数据的计算机仿真验证了改进方法的有效性和可用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dish extraction method with neural network for food intake measuring system on medical use
We have been engaging development of a food intake measuring system. This system measures amounts of food intake for each dish on the tray. Therefore, this system needs to extract dish image from a tray image. In this paper, we propose a dish extraction method by neural network (NN). We expect that the proposed method can extract dish image efficiently and exactly even if dishes are over-wrapped each other. While, food sometimes causes miss-recognition of the correct position of the extracted dish image, we newly add food rejection algorithm to the dish extraction method. Finally, we show the effectiveness and usability of the improved proposed method with computer simulation using real data.
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