@InProceedings{FonsecaNegPerCouGui:2021:ToAuMa,
               author = "Fonseca, Gabriel Barbosa da and Negrel, Romain and Perret, 
                         Benjamin and Cousty, Jean and Guimar{\~a}es, Silvio Jamil 
                         Ferzoli",
          affiliation = "{Pontif{\'{\i}}cia Universidade Cat{\'o}lica de Minas Gerais  } 
                         and {ESIEE Paris  } and LIGM, Universit{\'e} Gustave Eiffel, 
                         CNRS, ESIEE Paris   and LIGM, Universit{\'e} Gustave Eiffel, 
                         CNRS, ESIEE Paris   and {Pontif{\'{\i}}cia Universidade 
                         Cat{\'o}lica de Minas Gerais}",
                title = "New hierarchy-based segmentation layer: towards automatic marker 
                         proposal",
            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 = "interactive image segmentation, automatic marker proposal, 
                         segmentation layer, deep learning.",
             abstract = "Image segmentation is an ill-posed problem by definition, as it is 
                         not always possible to automatically select which object appearing 
                         in an image is the object of interest. To deal with this issue, 
                         prior knowledge in the form of human-given markers can be included 
                         in the segmentation pipeline. Even though user interaction can 
                         drastically improve segmentation results, it is an expensive 
                         resource, and finding ways to reduce human effort on an 
                         interactive segmentation loop is of great interest. In this work, 
                         we propose a new segmentation layer to be used with deep neural 
                         networks, which allows us to create and train in an end-to-end 
                         fashion a marker creation network. To train the network, we 
                         propose a loss function composed of: a segmentation loss using the 
                         proposed differentiable segmentation layer; and a set of 
                         regularization functions that enforce the desired characteristics 
                         on the produced markers. We showed that by using the proposed 
                         layer and loss function, we can train the network to automatically 
                         generate markers that recover a good segmentation and have 
                         desirable shape characteristics. This behavior is observed on the 
                         training dataset, as well as on four unseen datasets.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00055",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00055",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45CS5L8",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45CS5L8",
           targetfile = "SIBGRAPI2021_learning_markers_CR2.pdf",
        urlaccessdate = "2024, May 06"
}