title: Automatic portion estimation and visual refinement in mobile dietary assessment.
abstract: As concern for obesity grows, the need for automated and accurate methods to monitor nutrient intake becomes essential as dietary intake provides a valuable basis for managing dietary imbalance. Moreover, as mobile devices with built-in cameras have become ubiquitous, one potential means of monitoring dietary intake is photographing meals using mobile devices and having an automatic estimate of the nutrient contents returned. One of the challenging problems of the image-based dietary assessment is the accurate estimation of food portion size from a photograph taken with a mobile digital camera. In this work, we describe a method to automatically calculate portion size of a variety of foods through volume estimation using an image. These "portion volumes" utilize camera parameter estimation and model reconstruction to determine the volume of food items, from which nutritional content is then extrapolated. In this paper, we describe our initial results of accuracy evaluation using real and simulated meal images and demonstrate the potential of our approach.
authors.affiliation: School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana USA.
authors.forename: Insoo
authors.initials: I
authors.lastname: Woo
authors.forename: Karl
authors.initials: K
authors.lastname: Otsmo
authors.forename: Sungye
authors.initials: S
authors.lastname: Kim
authors.forename: David S
authors.initials: DS
authors.lastname: Ebert
authors.forename: Edward J
authors.initials: EJ
authors.lastname: Delp
authors.forename: Carol J
authors.initials: CJ
authors.lastname: Boushey