@InProceedings{MontagnerHiraJr:2016:ImOpLe,
               author = "Montagner, Igor S. and Hirata, Nina S. T. and Jr, Roberto Hirata",
          affiliation = "{University of S{\~a}o Paulo} and {University of S{\~a}o Paulo} 
                         and {University of S{\~a}o Paulo}",
                title = "Image operator learning and applications",
            booktitle = "Proceedings...",
                 year = "2016",
               editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and 
                         Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson 
                         A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti, 
                         David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa, 
                         Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and 
                         Santos, Jefersson dos and Schwartz, William Robson and Thomaz, 
                         Carlos E.",
         organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Image Operator learning, W-operators, Image Processing, Machine 
                         Learning.",
             abstract = "High-level understanding of image contents has been receiving much 
                         attention in the last decade. Low level processing figures as a 
                         building block in this framework and it also continues to play an 
                         important role in several specific tasks such as in image 
                         filtering and colorization, medical imaging, and document image 
                         processing. The design of image operators for these tasks is 
                         usually done manually by exploiting characteristics specific to 
                         the domain of application. An alternative design approach is to 
                         use machine learning techniques to estimate the transformations. 
                         Given pairs of images consisting of a typical input and respective 
                         desired output, the goal is to estimate an operator that 
                         transforms the inputs into the desired outputs. In this tutorial 
                         we present a rigorous mathematical formulation to the framework of 
                         learning locally defined and translation invariant 
                         transformations, practical procedures and strategies to address 
                         typical machine learning related issues, application examples, and 
                         current challenges. We also include information about the code 
                         used to generate the application examples.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
             language = "en",
                  ibi = "8JMKD3MGPAW/3MK9KBS",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3MK9KBS",
           targetfile = "tutorial-final.pdf",
        urlaccessdate = "2024, May 03"
}