Advertisement

Multi-view Model Contour Matching Based Food Volume Estimation

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 787)

Abstract

In this paper, an automatic food volume estimation method based on outer contour matching is proposed, which avoids the complicated calculation. We pre-defined a simple 3D model library and stored the projections and the user’s hand. Users took three images containing their hands from three views. The contour of segmented image was compared with the projections to find the best match. Meanwhile, we took the user’s hand as the scale and calculated the volume. As the method is easy to operate, less space-consuming, it is quite suitable for integrated application in the mobile app.

Keywords

Food volume estimation Image segmentation Contour matching Image registration Mobile application 

Notes

Acknowledgment

The research work described in this paper was fully supported by the grants from the National Natural Science Foundation of China (Project No. 61472043), National Key Research and Development Program Project: “The key technologies research and integrated demonstration of mountain torrent disaster monitoring and early warning (2017YFC1502505). Prof. Xin Zheng and Qian Yin are the authors to whom all correspondence should be addressed.

References

  1. 1.
    World Health Organization, February 2018. Obesity and overweight. http://www.who.int/mediacentre/factsheets/fs311/en/
  2. 2.
    World Health Organisation, February 2018. Obesity and overweight. http://www.who.int/mediacentre/factsheets/fs311/zh/
  3. 3.
    Puri, M., Zhu, Z., Yu, Q., Divakaran, A., Sawhney, H.: Recognition and volume estimation of food intake using a mobile device. Sarnoff Corporation (2009)Google Scholar
  4. 4.
    Jia, W., Yue, Y., Fernstrom, J.D., Zhang, Z., Yang, Y., Sun, M.: 3D localization of circular feature in 2D image and application to food volume estimation. In: 34th Annual International Conference of the IEEE EMBSGoogle Scholar
  5. 5.
    Image moment – Wikipedia. https://en.wikipedia.org/wiki/Image_moment

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Image Processing and Pattern Recognition LaboratoryBeijing Normal UniversityBeijingChina

Personalised recommendations