使用几何模型和深度图像的食物分量大小估计的比较。

Shaobo Fang, Fengqing Zhu, Chufan Jiang, Song Zhang, Carol J Boushey, Edward J Delp
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引用次数: 32

摘要

美国十大主要死因中有六个与饮食直接相关,包括癌症、糖尿病和心脏病。膳食摄入,即决定一个人一天中吃什么的过程,为制定预防上述许多慢性疾病的干预计划提供了宝贵的见解。测量准确的膳食摄入量被认为是营养和健康领域的一个开放的研究问题。在本文中,我们比较了两种从食物图像中估计食物份量的技术。这些技术基于三维几何模型和深度图像。开发了一种基于期望最大化的技术来检测深度图像中的参考平面,这对于使用深度图像的部分大小估计至关重要。我们的实验结果表明,与使用深度图像的估计相比,基于几何模型的体积估计对于具有明确三维形状的物体更准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A COMPARISON OF FOOD PORTION SIZE ESTIMATION USING GEOMETRIC MODELS AND DEPTH IMAGES.

A COMPARISON OF FOOD PORTION SIZE ESTIMATION USING GEOMETRIC MODELS AND DEPTH IMAGES.

A COMPARISON OF FOOD PORTION SIZE ESTIMATION USING GEOMETRIC MODELS AND DEPTH IMAGES.

Six of the ten leading causes of death in the United States, including cancer, diabetes, and heart disease, can be directly linked to diet. Dietary intake, the process of determining what someone eats during the course of a day, provides valuable insights for mounting intervention programs for prevention of many of the above chronic diseases. Measuring accurate dietary intake is considered to be an open research problem in the nutrition and health fields. In this paper we compare two techniques to estimating food portion size from images of food. The techniques are based on 3D geometric models and depth images. An expectation-maximization based technique is developed to detect the reference plane in depth images, which is essential for portion size estimation using depth images. Our experimental results indicate that volume estimation based on geometric model is more accurate for objects with well-defined 3D shapes compared to estimation using depth images.

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