Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2016: administrator
Metadata Last Update2019: administrator
Citation KeyMansillaMira:2016:CoCoAd
TitleOriented Image Foresting Transform Segmentation: Connectivity Constraints with Adjustable Width
Access Date2021, Jan. 27
Number of Files1
Size3846 KiB
Context area
Author1 Mansilla, Lucy A. C.
2 Miranda, Paulo A. V.
Affiliation1 Department of Computer Science, University of São Paulo (USP)
2 Department of Computer Science, University of São Paulo (USP)
EditorAliaga, Daniel G.
Davis, Larry S.
Farias, Ricardo C.
Fernandes, Leandro A. F.
Gibson, Stuart J.
Giraldi, Gilson A.
Gois, João Paulo
Maciel, Anderson
Menotti, David
Miranda, Paulo A. V.
Musse, Soraia
Namikawa, Laercio
Pamplona, Mauricio
Papa, João Paulo
Santos, Jefersson dos
Schwartz, William Robson
Thomaz, Carlos E.
Conference NameConference on Graphics, Patterns and Images, 29 (SIBGRAPI)
Conference LocationSão José dos Campos
DateOct. 4-7, 2016
Book TitleProceedings
PublisherIEEE Computer Society´s Conference Publishing Services
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2016-07-14 18:16:45 :: -> administrator ::
2016-10-05 14:49:12 :: administrator -> :: 2016
2016-10-13 19:18:18 :: -> administrator :: 2016
2019-11-28 23:15:39 :: administrator -> :: 2016
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Keywordsimage segmentation, connectivity constraints, boundary polarity, image foresting transform.
AbstractIn this work, we extend a novel seed-based segmentation algorithm, which provides global optimum solutions according to a graph-cut measure, subject to high-level boundary constraints: The simultaneously handling of boundary polarity and connectivity constraints. The proposed method incorporates the connectivity constraint in the Oriented Image Foresting Transform (OIFT), ensuring the generation of connected objects, but such that the connection between its internal seeds is guaranteed to have a user-controllable minimum width. In other frameworks, such as the min-cut/max-flow algorithm, the connectivity constraint is known to lead to NP-hard problems. In contrast, our method conserves the low complexity of the OIFT algorithm. In the experiments, we show improved results for the segmentation of thin and elongated objects, for the same amount of user interaction. Our dataset of natural images with true segmentation is publicly available to the community.
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Next Higher Units8JMKD3MGPAW/3M2D4LP
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