Paper Info
Title
Food Volume Computation For Self Dietary Assessment Applications
Abstract
There is great demand for easily-accessible, user-friendly dietary self-management applications. Yet accurate, fully-automatic estimation of nutritional intake using computer vision methods remains an open research problem. One key element of this problem is the volume estimation, which can be computed from 3D models obtained using multi-view geometry. The paper presents a computational system for volume estimation based on the processing of two meal images. A 3D model of the served meal is reconstructed using the acquired images and the volume is computed from the shape. The algorithm was tested on food models (dummy foods) with known volume and on real served food. Volume accuracy was in the order of 90 %, while the total execution time was below 15 seconds per image pair. The proposed system combines simple and computational affordable methods for 3D reconstruction, remained stable throughout the experiments, operates in near real time, and places minimum constraints on users.
Year
Venue
Keywords
2013
2013 IEEE 13TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE)
image reconstruction,computer vision,health care
Field
DocType
ISSN
Iterative reconstruction,Computer vision,Computer science,Volume Accuracy,Execution time,Volume estimation,Artificial intelligence,Dietary assessment,Computation,3D reconstruction
Conference
2471-7819
Citations 
PageRank 
References 
10
0.66
6
Authors
4
Name
Order
Citations
PageRank
Joachim Dehais1413.92
Sergey Shevchik2151.16
Peter Diem3655.19
Stavroula G Mougiakakou434228.61