@InProceedings{CastaņedaLeonVech:2017:MuSeHi,
               author = "Castaņeda Leon, Leissi Margarita and Vechiatto de Miranda, Paulo 
                         Andr{\'e}",
          affiliation = "Institute of Mathematics and Statistics, University of S{\~a}o 
                         Paulo and Institute of Mathematics and Statistics, University of 
                         S{\~a}o Paulo",
                title = "Multi-Object Segmentation by Hierarchical Layered Oriented Image 
                         Foresting Transform",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Multi-object segmentation, Image Foresting Transform.",
             abstract = "This 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.",
  conference-location = "Niter{\'o}i, RJ, Brazil",
      conference-year = "17-20 Oct. 2017",
                  doi = "10.1109/SIBGRAPI.2017.17",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.17",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PFRG3B",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3PFRG3B",
           targetfile = "2017_sibgrapi_LeissiCL.pdf",
        urlaccessdate = "2024, Apr. 28"
}