A New Approach to Image-Based Estimation of Food Volume
A New Approach to Image-Based Estimation of Food Volume
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DOI：
10.3390/a10020066
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发表时间：
2017-06-01
期刊：
影响因子：
2.3
通讯作者：
Cagnoni, Stefano
中科院分区：
文献类型：
--
作者：
Hassannejad, Hamid;Matrella, Guido;Cagnoni, Stefano
均衡的饮食是健康生活方式的关键，对于预防或治疗糖尿病和肥胖等许多慢性疾病至关重要。因此，监测饮食是改善人们健康的有效途径。然而，手工报告食物摄入量已被证明是不准确的，而且往往不切实际。本文提出了一种基于图像建模的食物摄取量估计方法。建模方法包括三个步骤：首先，用用户的智能手机拍摄食物的短视频。从这样的视频中，根据智能手机的方向传感器确定的图片视角选择6帧。其次，用户标记其中一帧以播种交互式分割算法。分割是基于高斯混合模型和图切算法。最后，一个定制的基于图像的建模算法生成一个点云来对食物进行建模。同时，随机目标检测方法定位棋盘作为尺寸/地面参考。对建模算法进行了优化，使得使用6个输入图像仍然可以产生可接受的计算成本。在我们的评估过程中，我们在包含不同种类面食和面包图像的测试集上实现了92%的平均准确率，平均处理时间约为23秒。
A balanced diet is the key to a healthy lifestyle and is crucial for preventing or dealing with many chronic diseases such as diabetes and obesity. Therefore, monitoring diet can be an effective way of improving people's health. However, manual reporting of food intake has been shown to be inaccurate and often impractical. This paper presents a new approach to food intake quantity estimation using image-based modeling. The modeling method consists of three steps: firstly, a short video of the food is taken by the user's smartphone. From such a video, six frames are selected based on the pictures' viewpoints as determined by the smartphone's orientation sensors. Secondly, the user marks one of the frames to seed an interactive segmentation algorithm. Segmentation is based on a Gaussian Mixture Model alongside the graph-cut algorithm. Finally, a customized image-based modeling algorithm generates a point-cloud to model the food. At the same time, a stochastic object-detection method locates a checkerboard used as size/ground reference. The modeling algorithm is optimized such that the use of six input images still results in an acceptable computation cost. In our evaluation procedure, we achieved an average accuracy of 92% on a test set that includes images of different kinds of pasta and bread, with an average processing time of about 23 s.