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2016 IEEE International Conference on Pervasive Computing and Communications (PerCom) (2016)
Sydney, Australia
March 14, 2016 to March 19, 2016
ISBN: 978-1-4673-8778-1
pp: 1-6
Junghyo Lee , IMPACT Lab, CIDSE, Arizona State University, Tempe, Az
Ayan Banerjee , IMPACT Lab, CIDSE, Arizona State University, Tempe, Az
Sandeep K. S. Gupta , IMPACT Lab, CIDSE, Arizona State University, Tempe, Az
ABSTRACT
In this paper, we propose MT-Diet, a smartphone-based automated diet monitoring system that interfaces a thermal camera with a smartphone and identifies types of food consumed at the click of a button. The system uses thermal maps of a food plate to increase accuracy of segmentation and extraction of food parts, and combines thermal and visual images to improve accuracy in the detection of cooked food. Test results on 80 different types of cooked food show that MT-Diet can isolate food parts with an accuracy of 97.5% and determine the type of food with an accuracy of 88.93%, which is a significant improvement (nearly 25%) over the state-of-the-art.
INDEX TERMS
Image segmentation, Image color analysis, Monitoring, Cameras, Color, Visualization, Image edge detection
CITATION

J. Lee, A. Banerjee and S. K. Gupta, "MT-Diet: Automated smartphone based diet assessment with infrared images," 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)(PERCOM), Sydney, Australia, 2016, pp. 1-6.
doi:10.1109/PERCOM.2016.7456506
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