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@InProceedings{MacariniWebe:2017:QuCoSy,
               author = "Macarini, Luiz Antonio and Weber, Tiago Oliveira",
          affiliation = "{Universidade Federal de Santa Catarina} and {Universidade Federal 
                         de Santa Catarina}",
                title = "Quality Control System for Ceramic Tiles using Segmentation-based 
                         Fractal Texture Analysis and SVM",
            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 = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Image Processing, Machine Learning, SFTA, Ceramic Tiles, Defect 
                         Detection.",
             abstract = "The ceramic industry has a highly automated production system. The 
                         quality control, however, is still performed by humans, which 
                         limits its speed and precision. This work proposes a complete 
                         verification system for ceramic tiles based on image processing 
                         and machine learning. The system has four steps: image 
                         acquisition, pre-processing, feature extraction and 
                         classification. The feature extraction step uses 
                         Segmentation-based Fractal Texture Analysis (SFTA). A Support 
                         Vector Machine is employed to classify the ceramic tiles. The 
                         system is implemented using OpenCV libraries. In total, 783 
                         ceramic tiles were used, being 80% for training and 20% to 
                         testing. The present work had reached the proposed objectives, 
                         both in processing time and accuracy, achieving 98.68% of 
                         detection rate.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PH2NFL",
                  url = "http://urlib.net/rep/8JMKD3MGPAW/3PH2NFL",
           targetfile = "SIBGRAPI_Luiz_Tiago.pdf",
        urlaccessdate = "2021, Jan. 19"
}


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