@InProceedings{KleineSantCappMira:2023:UnImSe,
               author = "Kleine, Felipe A. S. and Santos, Luiz F. D. and Cappabianco, 
                         F{\'a}bio A. M. and Miranda, Paulo A. V.",
          affiliation = "IPT - Institute for Technological Research of the State of 
                         S{\~a}o Paulo, Brazil and University of S{\~a}o Paulo, Institute 
                         of Mathematics and Statistics, S{\~a}o Paulo, SP, Brazil and 
                         Instituto de Ci{\^e}ncia e Tecnologia, S{\~a}o Jos{\'e} dos 
                         Campos, SP, Brazil and University of S{\~a}o Paulo, Institute of 
                         Mathematics and Statistics, S{\~a}o Paulo, SP, Brazil",
                title = "Unsupervised Image Segmentation by Oriented Image Foresting 
                         Transform in Layered Graphs",
            booktitle = "Proceedings...",
                 year = "2023",
               editor = "Clua, Esteban Walter Gonzalez and K{\"o}rting, Thales Sehn and 
                         Paulovich, Fernando Vieira and Feris, Rogerio",
         organization = "Conference on Graphics, Patterns and Images, 36. (SIBGRAPI)",
             keywords = "unsupervised image segmentation, image foresting transform, nested 
                         objects.",
             abstract = "In this work, we address the problem of unsupervised image 
                         segmentation, subject to high-level constraints expected for the 
                         objects of interest. More specifically, we handle the segmentation 
                         of a hierarchy of objects with nested boundaries, each with its 
                         own expected boundary polarity constraint. To this end, this work 
                         successfully extends Hierarchical Layered Oriented Image Foresting 
                         Transform (HLOIFT), with the inclusion of nested object relations, 
                         to the unsupervised segmentation paradigm. On the other hand, this 
                         work can also be seen as an extension of Unsupervised OIFT (UOIFT) 
                         to include structural relationships of nested objects. The method 
                         is demonstrated in the segmentation of three datasets of colored 
                         images with superior performance compared to other existing 
                         techniques in graphs, requiring a smaller number of connected 
                         partitions to isolate the objects of interest in the images.",
  conference-location = "Rio Grande, RS",
      conference-year = "Nov. 06-09, 2023",
                  doi = "10.1109/SIBGRAPI59091.2023.10347172",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI59091.2023.10347172",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/49L4DLS",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/49L4DLS",
           targetfile = "Kleine-103.pdf",
        urlaccessdate = "2024, Apr. 27"
}