| Automatic portion estimation and visual refinement in mobile dietary assessment. | |
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MedLine Citation:
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PMID: 22242198 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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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. |
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Authors:
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Insoo Woo; Karl Otsmo; Sungye Kim; David S Ebert; Edward J Delp; Carol J Boushey |
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Publication Detail:
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Type: JOURNAL ARTICLE |
Journal Detail:
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Title: Proceedings of SPIE Volume: 7533 ISSN: 0277-786X ISO Abbreviation: - Publication Date: 2010 Jan |
Date Detail:
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Created Date: 2012-1-13 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101524122 Medline TA: Proc SPIE Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Affiliation:
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School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana USA. |
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Descriptor/Qualifier:
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| Grant Support | |
ID/Acronym/Agency:
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R01 DK073711-01A1//NIDDK NIH HHS; U01 CA130784-01//NCI NIH HHS |
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