@InProceedings{SoaresEMMMPJB:2023:SeReMa,
               author = "Soares, Luciane Baldassari and Evangelista, Eduardo and Maurente, 
                         Vinicius and Machado, Matheus and Maurell, Igor and Pias, Marcelo 
                         and Jr, Paulo Drews and Botelho, Silvia",
          affiliation = "{Universidade Federal do Rio Grande - FURG} and {Universidade 
                         Federal do Rio Grande - FURG} and {Universidade Federal do Rio 
                         Grande - FURG} and {Universidade Federal do Rio Grande - FURG} and 
                         {Universidade Federal do Rio Grande - FURG} and {Universidade 
                         Federal do Rio Grande - FURG} and {Universidade Federal do Rio 
                         Grande - FURG} and {Universidade Federal do Rio Grande - FURG}",
                title = "Segmentation and Removal of Markings in Metal Inspection Images",
            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 = "Inpainting, segmentation, inspection images.",
             abstract = "The inspection process of metallic surfaces, especially FPSO 
                         tanks, is still heavily reliant on manual methods, requiring long 
                         production downtime and posing health risks to inspectors. 
                         Automating this analysis step will provide significant benefits to 
                         the management of these vessels' integrity, reducing expenses, 
                         downtime, and, most importantly, the exposure time of employees to 
                         hazards associated with inspection activities. During manual 
                         inspections, inspectors make annotations using paint, typically in 
                         white and yellow colors, directly on the tank walls, hindering the 
                         automation of the inspection process as it complicates the 
                         segmentation and identification of potential flaws on the tank 
                         wall using techniques such as neural network models. Recognizing 
                         this problem, this work presents a proposal for the identification 
                         and segmentation of these markings by segmenting them in the 
                         images, followed by the removal of the segmented markings using 
                         image texture-filling techniques.",
  conference-location = "Rio Grande, RS",
      conference-year = "Nov. 06-09, 2023",
                  doi = "10.1109/SIBGRAPI59091.2023.10347152",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI59091.2023.10347152",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/49LJ6AH",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/49LJ6AH",
           targetfile = "SOARES-101.pdf",
        urlaccessdate = "2024, May 06"
}