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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier6qtX3pFwXQZG2LgkFdY/Rwwsf
Repositorysid.inpe.br/sibgrapi@80/2007/09.21.12.42
Last Update2007:09.21.12.42.18 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi@80/2007/09.21.12.42.19
Metadata Last Update2022:05.18.22.21.15 (UTC) administrator
Citation KeyBastosConc:2007:AuTeSe
TitleAutomatic Texture Segmentation Based on k-means Clustering and Co-occurrence Features
FormatOn-line
Year2007
Access Date2024, Oct. 04
Number of Files1
Size90 KiB
2. Context
Author1 Bastos, Lucas
2 Conci, Aura
Affiliation1 Universidade Federal Fluminense
2 Universidade Federal Fluminense
EditorGonçalves, Luiz
Wu, Shin Ting
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 20 (SIBGRAPI)
Conference LocationBelo Horizonte, MG, Brazil
Date7-10 Oct. 2007
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeTechnical Poster
History (UTC)2008-07-17 14:09:45 :: aconci@ic.uff.br -> administrator ::
2009-08-13 20:38:44 :: administrator -> banon ::
2010-08-28 20:02:32 :: banon -> administrator ::
2022-05-18 22:21:15 :: administrator -> :: 2007
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsHaralick features
automatic texture segmentation
grey level co-occurrence
AbstractThis work presents a method for automatic texture segmentation based on k-means clustering technique and cooccurrence texture features. A set of up to eight features were extracted from a 256 grey-level co-occurrence information. These features were used to segment image regions regarding the textural homogeneity of its areas. As the process of calculating co-occurrence information demands the majority of computational time required,we propose a new methodology based on a grey-level cooccurrence indexed list (GLCIL) for fast element access and highly optimize this step in the algorithm. Besides that, we compare the efficiency of the proposed method against the GLCM and GLCLL algorithms. The GLCIL shows to be the most efficient method in terms of computational time. Additionally, traditional Brodatz textures and others examples of the literature were tested to evaluate the appropriateness and robustness of the method.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2007 > Automatic Texture Segmentation...
doc Directory Contentaccess
source Directory Contentthere are no files
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/6qtX3pFwXQZG2LgkFdY/Rwwsf
zipped data URLhttp://urlib.net/zip/6qtX3pFwXQZG2LgkFdY/Rwwsf
Languageen
Target Filelucasfinal.pdf
User Groupaconci@ic.uff.br
administrator
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/sibgrapi@80/2007/08.02.16.22
Next Higher Units8JMKD3MGPEW34M/46SF8Q5
Citing Item Listsid.inpe.br/sibgrapi/2022/05.14.00.14 53
sid.inpe.br/banon/2001/03.30.15.38.24 7
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 e-mailaddress 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


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