@InProceedings{BelémPerCouGuiFal:2021:ToSiEf,
               author = "Bel{\'e}m, Felipe de Castro and Perret, Benjamin and Cousty, Jean 
                         and Guimar{\~a}es, Silvio Jamil Ferzoli and Falc{\~a}o, 
                         Alexandre Xavier",
          affiliation = "{University of Campinas    } and {Universit{\'e} Gustave 
                         Eiffel    } and {Universit{\'e} Gustave Eiffel    } and 
                         {Pontifical Catholic University of Minas Gerais    } and 
                         {University of Campinas}",
                title = "Towards a Simple and Efficient Object-based Superpixel Delineation 
                         Framework",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "object-based,Image Foresting 
                         Transform,Superpixels,Saliency,Segmentation.",
             abstract = "Superpixel segmentation methods are widely used in computer vision 
                         applications due to their properties in border delineation. These 
                         methods do not usually take into account any prior object 
                         information. Although there are a few exceptions, such methods 
                         significantly rely on the quality of the object information 
                         provided and present high computational cost in most practical 
                         cases. Inspired by such approaches, we propose Object-based 
                         Dynamic and Iterative Spanning Forest (ODISF), a novel 
                         object-based superpixel segmentation framework to effectively 
                         exploit prior object information while being robust to the quality 
                         of that information. ODISF consists of three independent steps: 
                         (i) seed oversampling; (ii) dynamic path-based superpixel 
                         generation; and (iii) object-based seed removal. After (i), steps 
                         (ii) and (iii) are repeated until the desired number of 
                         superpixels is finally reached. Experimental results show that 
                         ODISF can surpass state-of-the-art methods according to several 
                         metrics, while being significantly faster than its object-based 
                         counterparts.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00054",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00054",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45E9P7S",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45E9P7S",
           targetfile = "2021_SIBGRAPI_ODISF.pdf",
        urlaccessdate = "2024, May 06"
}