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FooDD:food detection dataset for calorie measurement using food images

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dc.contributor.author Pouladzadeh, Parisa
dc.contributor.author Yassine, Abdulsalam
dc.contributor.author Shirmohammadi, Shervin
dc.date.accessioned 2016-08-02T12:17:49Z
dc.date.available 2016-08-02T12:17:49Z
dc.date.issued 2015-08
dc.identifier.isbn 9783319232218
dc.identifier.issn 03029743
dc.identifier.uri http://link.springer.com/chapter/10.1007%2F978-3-319-23222-5_54#page-1
dc.identifier.uri http://hdl.handle.net/11498/31545
dc.description.abstract Food detection, classification, and analysis have been the topic of indepth studies for a variety of applications related to eating habits and dietary assessment. For the specific topic of calorie measurement of food portions with single and mixed food items, the research community needs a dataset of images for testing and training. In this paper we introduce FooDD: a Food Detection Dataset of 3000 images that offer variety of food photos taken from different cameras with different illuminations. We also provide examples of food detection using graph cut segmentation and deep learning algorithms. en_US
dc.language.iso eng en_US
dc.publisher Springer Verlag en_US
dc.rights info:eu-repo/semantics/embargoedAccess en_US
dc.subject Calorie Measurement en_US
dc.subject Food Detection en_US
dc.subject Dietary Assessments en_US
dc.subject Eating Habits en_US
dc.subject Kalori Ölçümü en_US
dc.subject Gıda Algılama en_US
dc.subject Diyet Değerlendirmesi en_US
dc.subject Yeme Alışkanlığı en_US
dc.title FooDD:food detection dataset for calorie measurement using food images en_US
dc.type Article en_US
dc.relation.journal 18th International Conference on Image Analysis and Processing en_US


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