The intended target of this V-Unit project, a collaboration with Intel-Pittsburgh, is to enhance study, diagnosis and treatment of obesity, a condition in which the natural energy reserve, stored in the fat of humans and other mammals, is increased to such a point that it promotes serious pathologic conditions. Accurate and passive acquisition of dietary data from free-living individuals is essential for a better understanding of the etiology of obesity and the development of effective weight management programs. Currently, self-reporting is the main method for data acquisition. Despite its wide application using questionnaires and structured interviews, numerous studies have demonstrated that data obtained by self-reporting seriously underestimate food intake, and thus do not accurately reflect the habitual behavior of individuals in real life. Accurate computer-based programs for food recognition have yet to exist.
In this project, we have collected an image database of more than 100 fast foods sold by 9 well-known restaurant chains and I independently studied the problem of identifying foods from videos of eating (recorded by a low-cost web camera) using some off-the-shelf vision and retrieval algorithms.
Through this V-Unit project, I have learned that as a computer scientist, I should often focus my research interest and technology development on solving the problems of our society and serving the needs of people. Also, influence from Jie Yang, Manuela Veloso, M. Bernardine Dias and our Intel collaborators has been tremendously helpful in my project.
