Chae Junghoon, Woo Insoo, Kim Sungye, Maciejewski Ross, Zhu Fengging, Delp Edward J, Boushey Carol J, Ebert David S
School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana USA.
Proc SPIE Int Soc Opt Eng. 2011 Feb 7;7873:78730K. doi: 10.1117/12.876669.
As obesity concerns mount, dietary assessment methods for prevention and intervention are being developed. These methods include recording, cataloging and analyzing daily dietary records to monitor energy and nutrient intakes. Given the ubiquity of mobile devices with built-in cameras, one possible means of improving dietary assessment is through photographing foods and inputting these images into a system that can determine the nutrient content of foods in the images. One of the critical issues in such the image-based dietary assessment tool is the accurate and consistent estimation of food portion sizes. The objective of our study is to automatically estimate food volumes through the use of food specific shape templates. In our system, users capture food images using a mobile phone camera. Based on information (i.e., food name and code) determined through food segmentation and classification of the food images, our system choose a particular food template shape corresponding to each segmented food. Finally, our system reconstructs the three-dimensional properties of the food shape from a single image by extracting feature points in order to size the food shape template. By employing this template-based approach, our system automatically estimates food portion size, providing a consistent method for estimation food volume.
随着对肥胖问题的担忧日益增加，用于预防和干预的饮食评估方法正在不断发展。这些方法包括记录、编目和分析每日饮食记录，以监测能量和营养摄入。鉴于内置摄像头的移动设备无处不在，一种改进饮食评估的可能方法是拍摄食物并将这些图像输入一个能够确定图像中食物营养成分的系统。这种基于图像的饮食评估工具的关键问题之一是准确且一致地估计食物份量大小。我们研究的目的是通过使用特定食物形状模板来自动估计食物体积。在我们的系统中，用户使用手机摄像头拍摄食物图像。基于通过对食物图像进行分割和分类所确定的信息（即食物名称和代码），我们的系统为每个分割出的食物选择一个特定的食物模板形状。最后，我们的系统通过提取特征点从单张图像重建食物形状的三维属性，以便对食物形状模板进行尺寸测量。通过采用这种基于模板的方法，我们的系统自动估计食物份量大小，为估计食物体积提供了一种一致的方法。
