Volume Estimation Using Food Specific Shape Templates in Mobile Image-Based Dietary Assessment.
As obesity concerns mount, dietary assessment methods for prevention and intervention are being developed. These methods include recording, cataloging and analyzing daily dietary records to monitor energy and nutrient intakes. Given the ubiquity of mobile devices with built-in cameras, one possible means of improving dietary assessment is through photographing foods and inputting these images into a system that can determine the nutrient content of foods in the images. One of the critical issues in such the image-based dietary assessment tool is the accurate and consistent estimation of food portion sizes. The objective of our study is to automatically estimate food volumes through the use of food specific shape templates. In our system, users capture food images using a mobile phone camera. Based on information (i.e., food name and code) determined through food segmentation and classification of the food images, our system choose a particular food template shape corresponding to each segmented food. Finally, our system reconstructs the three-dimensional properties of the food shape from a single image by extracting feature points in order to size the food shape template. By employing this template-based approach, our system automatically estimates food portion size, providing a consistent method for estimation food volume.
School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana USA.
Junghoon
J
Chae
Insoo
I
Woo
Sungye
S
Kim
Ross
R
Maciejewski
Fengging
F
Zhu
Edward J
EJ
Delp
Carol J
CJ
Boushey
David S
DS
Ebert