Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2006: (UTC) administrator
Metadata Last Update2020: (UTC) administrator
Citation KeyMirandaBergRochFalc:2006:NeAlCo
TitleTree-Pruning: A New Algorithm and Its Comparative Analysis with theWatershed Transform for Automatic Image Segmentation
Access Date2022, Jan. 24
Number of Files1
Size573 KiB
Context area
Author1 Miranda, Paulo André Vechiatto de
2 Bergo, Felipe Paulo Guazzi
3 Rocha, Leonardo Marques
4 Falcao, Alexandre Xavier
Affiliation1 LIV, Institute of Computing, State University of Campinas
2 LIV, Institute of Computing, State University of Campinas
3 DECOM, FEEC, State University of Campinas
4 LIV, Institute of Computing, State University of Campinas
EditorOliveira Neto, Manuel Menezes de
Carceroni, Rodrigo Lima
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 19 (SIBGRAPI)
Conference LocationManaus
Date8-11 Oct. 2006
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2006-07-19 19:06:29 :: alexandre.falcao -> banon ::
2006-08-30 21:55:57 :: banon -> alexandre.falcao ::
2008-07-17 14:11:03 :: alexandre.falcao -> administrator ::
2009-08-13 20:38:08 :: administrator -> banon ::
2010-08-28 20:02:24 :: banon -> administrator ::
2020-02-19 03:17:41 :: administrator -> :: 2006
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Keywordsimage segmentation
license plate detection
image foresting transform
watershed transform
image analysis
AbstractImage segmentation using tree pruning (TP) and watershed (WS) has been presented in the framework of the image forest transform (IFT) a method to reduce image processing problems related to connectivity into an optimumpath forest problem in a graph. Given that both algorithms use the IFT with similar parameters, they usually produce similar segmentation results. However, they rely on different properties of the IFT which make TP more robust than WS for automatic segmentation tasks. We propose and demonstrate an important improvement in the TP algorithm, clarify the differences between TP and WS, and provide their comparative analysis from the theoretical and practical points of view. The experiments involve automatic segmentation of license plates in a database with 990 images.
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Target Filemiranda-treepruning.pdf
User Groupalexandre.falcao
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