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MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam...最新文献

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DeepFoodCam: A DCNN-based Real-time Mobile Food Recognition System DeepFoodCam:基于dcnn的实时移动食品识别系统
Ryosuke Tanno, Koichi Okamoto, Keiji Yanai
Due to the recent progress of the studies on deep learning, deep convolutional neural network (DCNN) based methods have outperformed conventional methods with a large margin. Therefore, DCNN-based recognition should be introduced into mobile object recognition. However, since DCNN computation is usually performed on GPU-equipped PCs, it is not easy for mobile devices where memory and computational power is limited.
由于近年来深度学习研究的进展,基于深度卷积神经网络(deep convolutional neural network, DCNN)的方法已经大大优于传统方法。因此,应该将基于dcnn的识别引入到移动目标识别中。然而,由于DCNN计算通常在配备gpu的pc上执行,因此对于内存和计算能力有限的移动设备来说并不容易。
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引用次数: 45
A Mobile Food Record For Integrated Dietary Assessment. 用于综合膳食评估的移动食物记录。
Ziad Ahmad, Deborah A Kerr, Marc Bosch, Carol J Boushey, Edward J Delp, Nitin Khanna, Fengqing Zhu

This paper presents an integrated dietary assessment system based on food image analysis that uses mobile devices or smartphones. We describe two components of our integrated system: a mobile application and an image-based food nutrient database that is connected to the mobile application. An easy-to-use mobile application user interface is described that was designed based on user preferences as well as the requirements of the image analysis methods. The user interface is validated by user feedback collected from several studies. Food nutrient and image databases are also described which facilitates image-based dietary assessment and enable dietitians and other healthcare professionals to monitor patients dietary intake in real-time. The system has been tested and validated in several user studies involving more than 500 users who took more than 60,000 food images under controlled and community-dwelling conditions.

本文介绍了一种基于食物图像分析的综合膳食评估系统,该系统使用移动设备或智能手机。我们介绍了综合系统的两个组成部分:移动应用程序和与移动应用程序连接的基于图像的食物营养素数据库。我们介绍了一个易于使用的移动应用程序用户界面,该界面是根据用户偏好以及图像分析方法的要求设计的。从几项研究中收集的用户反馈验证了该用户界面。此外,还介绍了食物营养素和图像数据库,这有助于基于图像的膳食评估,使营养师和其他医疗保健专业人员能够实时监测病人的膳食摄入量。该系统已在多项用户研究中进行了测试和验证,涉及 500 多名用户,他们在受控和社区居住条件下拍摄了 60,000 多张食物图像。
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引用次数: 0
Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management 第二届多媒体辅助饮食管理国际研讨会论文集
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引用次数: 4
期刊
MADiMa'16 : proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management : October 16, 2016, Amsterdam, The Netherlands. International Workshop on Multimedia Assisted Dietary Management (2nd : 2016 : Amsterdam...
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