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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/43B8HEB
Repositorysid.inpe.br/sibgrapi/2020/09.28.23.57
Last Update2020:09.28.23.57.06 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2020/09.28.23.57.06
Metadata Last Update2022:06.14.00.00.10 (UTC) administrator
DOI10.1109/SIBGRAPI51738.2020.00039
Citation KeyFreitasCordMaca:2020:FoSeCl
TitleMyFood: A Food Segmentation and Classification System to Aid Nutritional Monitoring
FormatOn-line
Year2020
Access Date2024, May 02
Number of Files1
Size6600 KiB
2. Context
Author1 Freitas, Charles N. C.
2 Cordeiro, Filipe R.
3 Macario, Valmir
Affiliation1 Universidade Federal Rural de Pernambuco
2 Universidade Federal Rural de Pernambuco
3 Universidade Federal Rural de Pernambuco
EditorMusse, Soraia Raupp
Cesar Junior, Roberto Marcondes
Pelechano, Nuria
Wang, Zhangyang (Atlas)
e-Mail Addressfilipe.rolim@ufrpe.br
Conference NameConference on Graphics, Patterns and Images, 33 (SIBGRAPI)
Conference LocationPorto de Galinhas (virtual)
Date7-10 Nov. 2020
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2020-09-28 23:57:06 :: filipe.rolim@ufrpe.br -> administrator ::
2022-06-14 00:00:10 :: administrator -> filipe.rolim@ufrpe.br :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsnutrition
food
segmentation
AbstractThe absence of food monitoring has contributed significantly to the increase in the populations weight. Due to the lack of time and busy routines, most people do not control and record what is consumed in their diet. Some solutions have been proposed in computer vision to recognize food images, but few are specialized in nutritional monitoring. This work presents the development of an intelligent system that classifies and segments food presented in images to help the automatic monitoring of user diet and nutritional intake. This work shows a comparative study of state-of-the-art methods for image classification and segmentation, applied to food recognition. In our methodology, we compare the FCN, ENet, SegNet, DeepLabV3+, and Mask RCNN algorithms. We build a dataset composed of the most consumed Brazilian food types, containing nine classes and a total of 1250 images. The models were evaluated using the following metrics: Intersection over Union, Sensitivity, Specificity, Balanced Precision, and Positive Predefined Value. We also propose a system integrated into a mobile application that automatically recognizes and estimates the nutrients in a meal, assisting people with better nutritional monitoring. The proposed solution showed better results than the existing ones in the market. The dataset is publicly available at the following link http://doi.org/10.5281/zenodo.4041488.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2020 > MyFood: A Food...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > MyFood: A Food...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/43B8HEB
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/43B8HEB
Languageen
Target FilePaper_ID_63_camara_ready_version_v2.pdf
User Groupfilipe.rolim@ufrpe.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/43G4L9S
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2020/10.28.20.46 7
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume
7. Description control
e-Mail (login)filipe.rolim@ufrpe.br
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