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@InProceedings{CruzCaSaPeLeCl:2017:ImAcAu,
               author = "Cruz, Rommel Anatoli Quintanilla and Cacau, Diego Carri{\c{c}}o 
                         and Santos, Renato Moraes dos and Pereira, Evandro Jose Ribeiro 
                         and Leta, Fabiana and Clua, Esteban",
          affiliation = "{Universidade Federal Fluminense} and {Universidade Federal 
                         Fluminense} and {LMDC - Universidade Federal Fluminense} and {LMDC 
                         - Universidade Federal Fluminense} and {LMDC - Universidade 
                         Federal Fluminense} and {Universidade Federal Fluminense}",
                title = "Improving accuracy of automatic fracture detection in borehole 
                         images with deep learning and GPUs",
            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 = "Deep learning, Automatic fracture detection, Pattern 
                         recognition.",
             abstract = "The logging and further analysis of borehole images is a major 
                         step in the interpretation of geological events. Natural fractures 
                         and beddings are features whose identification is commonly 
                         performed using acoustic and electrical borehole imaging tools. 
                         Such identification is a tedious task and is made visually by 
                         geologists, who must be experts on classification. The correct 
                         identification of planar features, represented as sinusoids into 
                         an image projection, depends on the quality of the images. Due to 
                         the distortions and noises of the images, known as artifacts, the 
                         automatic features detection is not trivial through conventional 
                         image processing methods. Since the identification process has to 
                         ensure that the marked events are true with minimal 
                         inconsistencies, we propose a pioneering approach to improving the 
                         quality of the results by applying deep neural networks to confirm 
                         or exclude candidate features extracted by a regular Hough 
                         transform. This is the first approach in literature to improve the 
                         quality of geological auto-detected marks by applying deep 
                         learning techniques for borehole images where our implementation 
                         is able to exclude most of the false positive marks.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
           targetfile = "PID4960365.pdf",
        urlaccessdate = "2021, Jan. 21"
}


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