Identity statement area | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Repository | sid.inpe.br/sibgrapi@80/2007/09.21.12.42 |
Last Update | 2007:09.21.12.42.18 administrator |
Metadata | sid.inpe.br/sibgrapi@80/2007/09.21.12.42.19 |
Metadata Last Update | 2020:02.19.03.06.19 administrator |
Citation Key | BastosConc:2007:AuTeSe |
Title | Automatic Texture Segmentation Based on k-means Clustering and Co-occurrence Features  |
Format | On-line |
Year | 2007 |
Date | Oct. 7-10, 2007 |
Access Date | 2021, Jan. 16 |
Number of Files | 1 |
Size | 90 KiB |
Context area | |
Author | 1 Bastos, Lucas 2 Conci, Aura |
Affiliation | 1 Universidade Federal Fluminense 2 Universidade Federal Fluminense |
Editor | Gonçalves, Luiz Wu, Shin Ting |
Conference Name | Brazilian Symposium on Computer Graphics and Image Processing, 20 (SIBGRAPI) |
Conference Location | Belo Horizonte |
Book Title | Proceedings |
Publisher | Sociedade Brasileira de Computação |
Publisher City | Porto Alegre |
Tertiary Type | Technical Poster |
History | 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 :: 2020-02-19 03:06:19 :: administrator -> :: 2007 |
Content and structure area | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Content Type | External Contribution |
Keywords | Haralick features,automatic texture segmentation, grey level co-occurrence. |
Abstract | This 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. |
source Directory Content | there are no files |
agreement Directory Content | there are no files |
Conditions of access and use area | |
data URL | http://urlib.net/rep/sid.inpe.br/sibgrapi@80/2007/09.21.12.42 |
zipped data URL | http://urlib.net/zip/sid.inpe.br/sibgrapi@80/2007/09.21.12.42 |
Language | en |
Target File | lucasfinal.pdf |
User Group | aconci@ic.uff.br administrator |
Visibility | shown |
Allied materials area | |
Mirror Repository | sid.inpe.br/sibgrapi@80/2007/08.02.16.22 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
Notes area | |
Empty Fields | accessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi e-mailaddress edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition nexthigherunit 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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