期刊
MEASUREMENT SCIENCE AND TECHNOLOGY
卷 24, 期 10, 页码 -出版社
IOP PUBLISHING LTD
DOI: 10.1088/0957-0233/24/10/105701
关键词
model-based framework; single view 3D/2D registration; active contour model; food volume estimation; quantitative dietary assessment
资金
- National Institutes of Health grant [U01 HL91736, R01CA165255]
- National Science Council, Taiwan, Republic of China
Dietary assessment is important in health maintenance and intervention in many chronic conditions, such as obesity, diabetes and cardiovascular disease. However, there is currently a lack of convenient methods for measuring the volume of food (portion size) in real-life settings. We present a computational method to estimate food volume from a single photographic image of food contained on a typical dining plate. First, we calculate the food location with respect to a 3D camera coordinate system using the plate as a scale reference. Then, the food is segmented automatically from the background in the image. Adaptive thresholding and snake modeling are implemented based on several image features, such as color contrast, regional color homogeneity and curve bending degree. Next, a 3D model representing the general shape of the food (e.g., a cylinder, a sphere, etc) is selected from a pre-constructed shape model library. The position, orientation and scale of the selected shape model are determined by registering the projected 3D model and the food contour in the image, where the properties of the reference are used as constraints. Experimental results using various realistically shaped foods with known volumes demonstrated satisfactory performance of our image-based food volume measurement method even if the 3D geometric surface of the food is not completely represented in the input image.
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