Close
Metadata

1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3S48B3H
Repositorysid.inpe.br/sibgrapi/2018/10.22.14.04
Last Update2018:10.22.14.04.11 (UTC) tamirisnegri@gmail.com
Metadata Repositorysid.inpe.br/sibgrapi/2018/10.22.14.04.11
Metadata Last Update2022:05.18.22.18.34 (UTC) administrator
Citation KeyFerrazBorCavGonSai:2018:EvCoNe
TitleEvaluation of convolutional neural networks for raw food texture classification under variations of lighting conditions
FormatOn-line
Year2018
Access Date2024, May 03
Number of Files1
Size1768 KiB
2. Context
Author1 Ferraz, Carolina Toledo
2 Borges, Tamiris T. N.
3 Cavichiolli, Adriane
4 Gonzaga, Adilson
5 Saito, José H.
Affiliation1 UNIFACCAMP
2 Federal Institute of São Paulo
3 University of São Paulo
4 University of São Paulo
5 UNIFACCAMP
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
e-Mail Addresstamirisnegri@gmail.com
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Date29 Oct.-1 Nov. 2018
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeWork in Progress
History (UTC)2018-10-22 14:04:11 :: tamirisnegri@gmail.com -> administrator ::
2022-05-18 22:18:34 :: administrator -> :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordstexture classification
CNN
light intensity
AbstractThis work is a preliminary evaluation of convolutional neural networks (CNN) applied to food texture classification, particularly when the texture is subject to changes in the lighting conditions. Four previously published CNN architectures (Alexnet, Resnet 18, Resnet 34 and Resnet 50) are investigated and compared to local descriptors designed specifically for this task. Although preliminary results indicate that the investigated CNN are outperformed by the descriptors, further analysis are required to investigate the impact of the experimental design adopted in this work-in-progress; especially in regard to the number of training samples and CNN configuration.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2018 > Evaluation of convolutional...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 22/10/2018 11:04 1.2 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3S48B3H
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3S48B3H
Languageen
Target Filesibgrapi_2018_versaofinal.pdf
User Grouptamirisnegri@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3RPADUS
Citing Item Listsid.inpe.br/sibgrapi/2018/09.03.20.37 9
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi 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 versiontype volume


Close