@InProceedings{BragantiniFalc:2022:GrAlFe,
               author = "Bragantini, Jord{\~a}o and Falc{\~a}o, Alexandre Xavier",
          affiliation = "{Chan Zuckerberge Biohub} and {University of Campinas}",
                title = "Interactive Image Segmentation: From Graph-based Algorithms to 
                         Feature-Space Annotation",
            booktitle = "Proceedings...",
                 year = "2022",
         organization = "Conference on Graphics, Patterns and Images, 35. (SIBGRAPI)",
             keywords = "image segmentation, interactive image segmentation, data 
                         annotation.",
             abstract = "In recent years, machine learning algorithms that solve problems 
                         from a collection of examples (i.e. labeled data), have grown to 
                         be the predominant approach for solving computer vision and image 
                         processing tasks. These algorithms performance is highly 
                         correlated with the abundance of examples and their quality, 
                         especially methods based on neural networks, which are 
                         significantly data-hungry. Notably, image segmentation annotation 
                         requires extensive effort to produce high-quality labeling due to 
                         the fine-scale of the units (pixels) and resorts to interactive 
                         methodologies to provide user assistance. Therefore, improving 
                         interactive image segmentation methodologies with the goal of 
                         improving data labeling problems is of paramount importance to 
                         advance applications of computer vision methods. With this in 
                         mind, we investigated the existing literature on interactive image 
                         segmentation, contributing to it by introducing novel algorithms 
                         that perform the segmentation from markers, contours, and finally 
                         proposing a new paradigm for image annotation at scale.",
  conference-location = "Natal, RN",
      conference-year = "24-27 Oct. 2022",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/47QK5P2",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/47QK5P2",
           targetfile = "2022_Bragantini_WTD_SIBGRAPI-3.pdf",
        urlaccessdate = "2024, May 02"
}