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Reference TypeConference Paper (Conference Proceedings)
Last Update2017: (UTC) administrator
Metadata Last Update2021: (UTC) administrator
Citation KeyCastañedaLeonVech:2017:MuSeHi
TitleMulti-Object Segmentation by Hierarchical Layered Oriented Image Foresting Transform
Access Date2022, Jan. 24
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Author1 Castañeda Leon, Leissi Margarita
2 Vechiatto de Miranda, Paulo André
Affiliation1 Institute of Mathematics and Statistics, University of São Paulo
2 Institute of Mathematics and Statistics, University of São Paulo
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
DateOct. 17-20, 2017
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2017-08-21 22:27:34 :: -> administrator ::
2021-02-23 03:53:09 :: administrator -> :: 2017
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Is the master or a copy?is the master
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Content TypeExternal Contribution
KeywordsMulti-object segmentation
Image Foresting Transform
AbstractThis paper introduces a new method for multi-object segmentation in images, named as Hierarchical Layered Oriented Image Foresting Transform (HLOIFT). As input, we have an image, a tree of relations between image objects, with the individual high-level priors of each object coded in its nodes, and the objects' seeds. Each node of the tree defines a weighted digraph, named as layer. The layers are then integrated by the geometric interactions, such as inclusion and exclusion relations, extracted from the given tree into a unique weighted digraph, named as hierarchical layered digraph. A single energy optimization is performed in the hierarchical layered weighted digraph by Oriented Image Foresting Transform (OIFT) leading to globally optimal results satisfying all the high-level priors. We evaluate our framework in the multi-object segmentation of medical and synthetic images, obtaining results comparable to the state-of-the-art methods, but with low computational complexity. Compared to multi-object segmentation by min-cut/max-flow algorithm, our approach is less restrictive, leading to globally optimal results in more general scenarios. > SDLA > SIBGRAPI 2017 > Multi-Object Segmentation by...
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