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Algorithms 2017, 10(2), 66; doi:10.3390/a10020066

A New Approach to Image-Based Estimation of Food Volume

Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Parma, 43124 Parma, Italy
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Academic Editor: Qianping Gu
Received: 19 April 2017 / Revised: 24 May 2017 / Accepted: 6 June 2017 / Published: 10 June 2017
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Abstract

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. View Full-Text
Keywords: automatic diet monitoring; image analysis; interactive segmentation; image-based modeling; volume estimation automatic diet monitoring; image analysis; interactive segmentation; image-based modeling; volume estimation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Hassannejad, H.; Matrella, G.; Ciampolini, P.; Munari, I.D.; Mordonini, M.; Cagnoni, S. A New Approach to Image-Based Estimation of Food Volume. Algorithms 2017, 10, 66.

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