@InProceedings{Condori:2017:ExDiIm,
               author = "Condori, Marcos Ademir Tejada",
          affiliation = "University of Sao Paulo, Institute of Mathematics and Statistics, 
                         Sao Paulo, SP, Brazil",
                title = "Extending the Differential Image Foresting Transform to Root-based 
                         Path-cost Functions with Application to Superpixel Segmentation",
            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 = "Image Foresting Transform, Superpixels, Differential Image 
                         Foresting Transform, IFT-SLIC.",
             abstract = "The Image Foresting Transform (IFT) is a general framework to 
                         develop image processing tools for a variety of tasks such as 
                         image segmentation, boundary tracking, morphological filters, 
                         pixel clustering, among others. The Differential Image Foresting 
                         Transform (DIFT) comes in handy for scenarios where multiple 
                         iterations of IFT over the same image with small modifications on 
                         the input parameters are expected, reducing the processing 
                         complexity from linear to sublinear with respect to the number of 
                         pixels. In this paper, we propose an enhanced variant of the DIFT 
                         algorithm that avoids inconsistencies, when the connectivity 
                         function is not monotonically incremental. Our algorithm works 
                         with the classical and non-classifical connectivity functions 
                         based on root position. Experiments were conducted on a superpixel 
                         task, showing a significant improvement to a state-of-the-art 
                         method.",
  conference-location = "Niter{\'o}i, RJ, Brazil",
      conference-year = "17-20 Oct. 2017",
                  doi = "10.1109/SIBGRAPI.2017.8",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.8",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PFS8RS",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3PFS8RS",
           targetfile = "condori148.pdf",
        urlaccessdate = "2024, Apr. 27"
}