@InProceedings{CalhesKobMatMacOli:2021:SiHoPi,
               author = "Calhes, Danilo and Kobayashi, Felipe K. and Mattos, Andrea Britto 
                         and Macedo, Maysa Malfiza Garcia de and Oliveira, Dario Augusto 
                         Borges",
          affiliation = "IBM  and {Federal University of ABC } and {IBM Research } and {IBM 
                         Research } and {IBM Research}",
                title = "Simplifying Horizon Picking Using Single-Class Semantic 
                         Segmentation Networks",
            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 = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "seismic image, horizon picking, deep learning, image 
                         classification.",
             abstract = "Seismic image processing plays a significant role in geological 
                         exploration as it conditions much of the interpretation 
                         performance. The interpretation process comprises several tasks, 
                         and Horizon Picking is one of the most time-consuming. Thereat, 
                         several works proposed methods for picking horizons automatically, 
                         mostly focusing on increasing the accuracy of data-driven 
                         approaches, by employing, for instance, semantic segmentation 
                         networks. However, these works often rely on a training process 
                         that requires several annotated samples, which are known to be 
                         scarce in the seismic domain, due to the overwhelming effort 
                         associated with manually picking several horizons in a seismic 
                         cube. This paper aims to evaluate the simplification of the 
                         labeling process required for training, by using training samples 
                         composed of disconnected horizons tokens, therefore relaxing the 
                         requirement of annotating the full set of horizons from each 
                         training sample, as commonly observed in previous works employing 
                         semantic segmentation networks. We assessed two state-of-art 
                         neural networks for general-purpose domains (PSP-Net and Deeplab 
                         V3+) using public seismic data (Netherlands F3 Block dataset). Our 
                         results report a minor impact in the performance using our 
                         proposed incomplete token training scheme compared to the complete 
                         one, moreover, we report that these networks outperform the 
                         current state-of-art for horizon picking from small training sets. 
                         Thus, our approach proves to be advantageous for the interpreter, 
                         given that using partial results instead of providing a full 
                         annotation can reduce the user effort during the labeling process 
                         required for training the models.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00046",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00046",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45CC8TE",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45CC8TE",
           targetfile = "Sibgrapi_2021___binary_horizon_picking-2.pdf",
        urlaccessdate = "2024, May 07"
}