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Reference TypeConference Paper (Conference Proceedings)
Last Update2015:
Metadata Last Update2016: administrator
Citation KeyMansillaMira:2015:ImSeIm
TitleImage Segmentation by Image Foresting Transform with Non-smooth Connectivity Functions
Access Date2021, Jan. 27
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Author1 Mansilla, Lucy Alsina Choque
2 Miranda, Paulo André Vechiatto de
EditorSegundo, Maurício Pamplona
Faria, Fabio Augusto
Conference NameConference on Graphics, Patterns and Images, 28 (SIBGRAPI)
Conference LocationSalvador
DateAug. 26-29, 2015
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Secondary TypeMaster's Work
Tertiary TypeMaster's or Doctoral Work
History2015-08-01 00:03:01 :: -> administrator ::
2016-06-03 21:18:38 :: administrator -> :: 2015
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Is the master or a copy?is the master
Content Stagecompleted
Keywordscut, image foresting transform, oriented image foresting transform, non-smooth connectivity function, geodesic star convexity.
AbstractImage segmentation, such as to extract an object from a background, is very useful for medical and biological image analysis. In this work, we propose new methods for interactive segmentation of multidimensional images, based on the Image Foresting Transform (IFT), by exploiting for the first time non-smooth connectivity functions (NSCF) with a strong theoretical background. The new algorithms provide global optimum solutions according to an energy function of graph cut, subject to high-level boundary constraints (polarity and shape), or consist in a sequence of paths' optimization in residual graphs. Our experimental results indicate substantial improvements in accuracy in relation to other state-of-the-art methods, by allowing the customization of the segmentation to a given target object.
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