Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2005: (UTC) administrator
Metadata Last Update2020: (UTC) administrator
Citation KeyFalcãoMiraRochBerg:2005:ObDeKc
TitleObject detection by k-connected seed competition
Access Date2021, Nov. 27
Number of Files1
Size231 KiB
Context area
Author1 Falcão, Alexandre Xavier
2 Miranda, Paulo André Vechiatto
3 Rocha, Anderson
4 Bergo, Felipe P. G.
Affiliation1 State University of Campinas
EditorRodrigues, Maria Andréia Formico
Frery, Alejandro César
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI)
Conference LocationNatal
Date9-12 Oct. 2005
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2005-06-27 15:41:47 :: anderson.rocha -> banon ::
2005-07-04 12:08:02 :: banon -> anderson.rocha ::
2008-07-17 14:10:58 :: anderson.rocha -> banon ::
2008-08-26 15:17:00 :: banon -> administrator ::
2009-08-13 20:37:41 :: administrator -> banon ::
2010-08-28 20:01:16 :: banon -> administrator ::
2020-02-19 03:19:05 :: administrator -> :: 2005
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
KeywordsKappa connectednes
seed competition
image segmentation
AbstractThe notion of Âstrength of connectedness between pixels has been successfully used in image segmentation. We present an extension to these works, which can considerably increase the efficiency of object definition tasks. A set of pixels is said a k-connected component with respect to a seed pixel when the strength of connectedness of any pixel in that set with respect to the seed is higher than or equal to a threshold. While the previous approaches either assume no competition with a single threshold for all seeds or eliminate the threshold for seed competition, we found that seed competition with different thresholds can reduce the number of seeds and the need for user interaction during segmentation. We also propose automatic and user-friendly interactive methods for determining the thresholds. The improvements are demonstrated through several segmentation experiments involving medical images.
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Target Filefalcaoa_seedcompetition.pdf
User Groupanderson.rocha
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