Identity statement area | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Identifier | 8JMKD3MGPAW/3PK5D5L |
Repository | sid.inpe.br/sibgrapi/2017/09.10.23.00 |
Last Update | 2017:09.10.23.00.26 perhark@ime.usp.br |
Metadata | sid.inpe.br/sibgrapi/2017/09.10.23.00.26 |
Metadata Last Update | 2020:02.20.22.06.48 administrator |
Citation Key | Murrugarra-LLerenaHira:2017:GaImCl |
Title | Galaxy image classification  |
Format | On-line |
Year | 2017 |
Access Date | 2021, Mar. 02 |
Number of Files | 1 |
Size | 476 KiB |
Context area | |
Author | 1 Murrugarra-LLerena, Joseph Hans 2 Hirata, Nina Sumiko Tomita |
Affiliation | 1 Institute of Mathematics and Statistics of the University of São Paulo, São Paulo, Brazil 2 Institute of Mathematics and Statistics of the University of São Paulo, São Paulo, Brazil |
Editor | Torchelsen, Rafael Piccin Nascimento, Erickson Rangel do Panozzo, Daniele Liu, Zicheng Farias, Mylène Viera, Thales Sacht, Leonardo Ferreira, Nivan Comba, João Luiz Dihl Hirata, Nina Schiavon Porto, Marcelo Vital, Creto Pagot, Christian Azambuja Petronetto, Fabiano Clua, Esteban Cardeal, Flávio |
e-Mail Address | perhark@ime.usp.br |
Conference Name | Conference on Graphics, Patterns and Images, 30 (SIBGRAPI) |
Conference Location | Niterói, RJ |
Date | Oct. 17-20, 2017 |
Book Title | Proceedings |
Publisher | Sociedade Brasileira de Computação |
Publisher City | Porto Alegre |
Tertiary Type | Work in Progress |
History | 2017-09-10 23:00:26 :: perhark@ime.usp.br -> administrator :: 2020-02-20 22:06:48 :: administrator -> :: 2017 |
Content and structure area | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Keywords | galaxy classification, deep learning, convolutional neural networks. |
Abstract | Over the years, different methods based either on morphological features or on expert knowledge have been proposed to classify galaxies. The amount of data to be processed in large scale surveys poses a new challenge for the classification. In this preliminary study, we investigate machine learning methods for galaxy image classification. Specifically, we evaluate convolutional neural networks as tools to be used in the classification process. Different ways of using convolutional neural networks has been experimented to classify galaxies as elliptical or spiral. Classification accuracy around 90-91% for the Sloan Digital Sky Survey (SDSS) galaxy images has been achieved. |
Arrangement | |
source Directory Content | there are no files |
agreement Directory Content | |
Conditions of access and use area | |
data URL | http://urlib.net/rep/8JMKD3MGPAW/3PK5D5L |
zipped data URL | http://urlib.net/zip/8JMKD3MGPAW/3PK5D5L |
Language | en |
Target File | article.pdf |
User Group | perhark@ime.usp.br |
Visibility | shown |
Update Permission | not transferred |
Allied materials area | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPAW/3PKCC58 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
Notes area | |
Empty Fields | accessionnumber archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume |
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