@InProceedings{WojcikMenoHill:2021:SeGrGe,
               author = "Wojcik, Lucas Matheus Leite and {Jorge Junior} and Menotti, David 
                         and Hill, Jo{\~a}o",
          affiliation = "{Federal University of Paran{\'a} (UFPR)} and {Federal University 
                         of Paran{\'a} (UFPR)} and {Federal University of Paran{\'a} 
                         (UFPR)} and {Institute of Rural Development of Paran{\'a} 
                         (IDR)}",
                title = "Segmentation and graph generation of muzzle images for cattle 
                         identification",
            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 = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Animal biometrics, Computer vision, Pattern recognition.",
             abstract = "The current methods for the organizing the records (i.e., 
                         cataloguing) of cattle are known to be archaic and inefficient, 
                         and often harmful to the animal. Such methods include the use of 
                         metal tags attached to the animal's ears like earrings and of 
                         branding irons on their necks. Previous research on new methods of 
                         livestock branding based on computer vision techniques utilized a 
                         mixture of texture features such as Gabor Filters and Local Binary 
                         Pattern as a means of extracting identifying features for each 
                         animal. The presented approach proposes a new technique using the 
                         muzzle image as an individual identifier as a novel technique, 
                         assuming that the muzzle RoI taken as input for the model pipeline 
                         is already extracted and cropped. This task is performed in three 
                         steps. First, the muzzle image is segmented via a convolutional 
                         neural network, resulting in a bitmap from which a graph structure 
                         is extracted in the second phase. The final phase consists of 
                         matching the resulting graph with the ones previously extracted 
                         and stored in the database for an optimal match. The results for 
                         the segmentation quality show a fidelity of around seventy 
                         percent, while the extracted graph perfectly represents the 
                         extracted bitmap. The matching algorithm is currently in 
                         progress.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45E886B",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45E886B",
           targetfile = "2021_WIP_IDR_SegmentMatch(4).pdf",
        urlaccessdate = "2024, Apr. 28"
}