Close

1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3DJ99KH
Repositorysid.inpe.br/sibgrapi/2013/02.17.22.28
Last Update2013:02.17.22.28.37 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2013/02.17.22.28.37
Metadata Last Update2022:06.17.21.39.29 (UTC) administrator
ISBN978-85-7669-273-7
Citation KeyMoreiraCost:1995:MuImSe
TitleMultispectral image segmentation by chromaticity classification
FormatImpresso, On-line.
Year1995
Access Date2024, May 01
Number of Files1
Size5777 KiB
2. Context
Author1 Moreira, Jander
2 Costa, Luciano da Fontoura
Affiliation1 Departamento de Computação da Universidade de São Carlos (UFSCar)
2 Instituto de Física de São Carlos (IFSC) da Universidade de São Paulo (USP)
EditorLotufo, Roberto de Alencar
Mascarenhas, Nelson Delfino d'Ávila
e-Mail Addresscintiagraziele.silva@gmail.com
Conference NameSimpósio Brasileiro de Computação Gráfica e Processamento de Imagens, 8 (SIBGRAPI)
Conference LocationSão Carlos, SP, Brazil
Date25-27 Oct. 1995
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Pages119-125
Book TitleAnais
Tertiary TypeArtigo
History (UTC)2013-02-17 22:28:37 :: cintiagraziele.silva@gmail.com -> administrator ::
2022-06-17 21:39:29 :: administrator -> cintiagraziele.silva@gmail.com :: 1995
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsimage segmentation
multispectral image segmentation
chromaticity classification
AbstractThis paper describes a color segmentation technique, based on the k-nearest-neighbor classification scheme, which operates on a normalized version of the color image known as the chromaticity image. An investigation was carried out in order to evaluate how the classification behaves for different number of neighbors (k), for distinct window sizes (in which an average of a sample feature is taken), and for various numbers of samples per class. The results, which are experimentally assessed by comparing the obtained classifications with a standard reference (segmented by human), shows that the method provides good overall accuracy and robustness. The class space for the test image is also presented in graphical form.
TypeSegmentação de Imagens
Arrangement 1urlib.net > SDLA > Fonds > Full Index > Multispectral image segmentation...
Arrangement 2urlib.net > SDLA > Fonds > SIBGRAPI 1995 > Sumário > Multispectral image segmentation...
Arrangement 3urlib.net > SDLA > Fonds > SIBGRAPI 1995 > Sumário > Índice > Multispectral image segmentation...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3DJ99KH
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3DJ99KH
Languageen
Target File15 Multispectral image.pdf
User Groupcintiagraziele.silva@gmail.com
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/sibgrapi@80/2007/08.02.16.22
Next Higher Units8JMKD3MGPEW34M/4742MCS
8JMKD3MGPBW34M/3DKGHN2
8JMKD3MGPBW34M/3DL29P8
Citing Item Listsid.inpe.br/sibgrapi/2013/02.25.18.00 2
sid.inpe.br/sibgrapi/2013/02.20.16.53 1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group issn label lineage mark nextedition notes numberofvolumes orcid organization parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark url versiontype volume
7. Description control
e-Mail (login)cintiagraziele.silva@gmail.com
update 


Close