A Novel Approach to Dining Bowl Reconstruction for Image-Based Food Volume Estimation
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Date
2022
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Journal ISSN
Volume Title
Publisher
MDPI
Abstract
Knowing the amounts of energy and nutrients in an individual’s diet is important for
maintaining health and preventing chronic diseases. As electronic and AI technologies advance
rapidly, dietary assessment can now be performed using food images obtained from a smartphone or
a wearable device. One of the challenges in this approach is to computationally measure the volume
of food in a bowl from an image. This problem has not been studied systematically despite the bowl
being the most utilized food container in many parts of the world, especially in Asia and Africa.
In this paper, we present a new method to measure the size and shape of a bowl by adhering a paper
ruler centrally across the bottom and sides of the bowl and then taking an image. When observed
from the image, the distortions in the width of the paper ruler and the spacings between ruler
markers completely encode the size and shape of the bowl. A computational algorithm is developed
to reconstruct the three-dimensional bowl interior using the observed distortions. Our experiments
using nine bowls, colored liquids, and amorphous foods demonstrate high accuracy of our method
for food volume estimation involving round bowls as containers. A total of 228 images of amorphous
foods were also used in a comparative experiment between our algorithm and an independent
human estimator. The results showed that our algorithm overperformed the human estimator who
utilized different types of reference information and two estimation methods, including direct volume
estimation and indirect estimation through the fullness of the bowl.
Description
Research Article
Keywords
3D reconstruction, food volume estimation, image-based dietary assessment, round bowl