title: Automatic detection of dining plates for image-based dietary evaluation.
abstract: An automatic detector that finds circular dining plates in chronically recorded images or videos is reported for the study of food intake and obesity. We first detect edges from input images. After a number of processing steps that convert edges into curves, arc filtering and grouping algorithms are applied. Then, convex hulls are identified and the ones that fit the description of ellipses corresponding to dining plates are determined. Our experiments using real-world images indicate that this detector is highly reliable and robust even when the input images contain complex background scenes and the dining plates are severely occluded.
authors.affiliation: Department of Computer Science, Ocean University of China, Qingdao, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Nie
authors.forename: Zhiqiang
authors.initials: Z
authors.lastname: Wei
authors.forename: Wenyan
authors.initials: W
authors.lastname: Jia
authors.forename: Lu
authors.initials: L
authors.lastname: Li
authors.forename: John D
authors.initials: JD
authors.lastname: Fernstrom
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Sclabassi
authors.forename: Mingui
authors.initials: M
authors.lastname: Sun
mesh_terms: D000465:Algorithms; D001331:Automation; D004032:Diet; D004864:Equipment and Supplies; D006801:Humans; D015596:Nutrition Assessment; D015203:Reproducibility of Results