@InProceedings{PaixãoPLLPTLL:2009:RaWaVe,
               author = "Paix{\~a}o, Jo{\~a}o and Petronetto, Fabiano and Lage, Marcos 
                         and Laier, Alex and Pesco, Sin{\'e}sio and Tavares, Geovan and 
                         Lewiner, Thomas and Lopes, H{\'e}lio",
          affiliation = "Matm{\'{\i}}dia Laboratory – Department of Mathematics, PUC–Rio 
                         – Rio de Janeiro, Brazil and Matm{\'{\i}}dia Laboratory – 
                         Department of Mathematics, PUC–Rio – Rio de Janeiro, Brazil and 
                         Matm{\'{\i}}dia Laboratory – Department of Mathematics, PUC–Rio 
                         – Rio de Janeiro, Brazil and Matm{\'{\i}}dia Laboratory – 
                         Department of Mathematics, PUC–Rio – Rio de Janeiro, Brazil and 
                         Matm{\'{\i}}dia Laboratory – Department of Mathematics, PUC–Rio 
                         – Rio de Janeiro, Brazil and Matm{\'{\i}}dia Laboratory – 
                         Department of Mathematics, PUC–Rio – Rio de Janeiro, Brazil and 
                         Matm{\'{\i}}dia Laboratory – Department of Mathematics, PUC–Rio 
                         – Rio de Janeiro, Brazil and Matm{\'{\i}}dia Laboratory – 
                         Department of Mathematics, PUC–Rio – Rio de Janeiro, Brazil",
                title = "Random Walks for Vector Field Denoising",
            booktitle = "Proceedings...",
                 year = "2009",
               editor = "Nonato, Luis Gustavo and Scharcanski, Jacob",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 22. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Discrete Vector Field, Denoising, Random Walk, Markov Chain.",
             abstract = "In recent years, several devices allow to directly measure real 
                         vector \fields, leading to a better understanding of 
                         fundamental phenomena such as \fluid simulation or brain 
                         water movement. This turns vector \field visualization and 
                         analysis important tools for many applications in engineering and 
                         in medicine. However, real data is generally corrupted by noise, 
                         puzzling the understanding provided by those tools. Those tools 
                         thus need a denoising step as preprocessing, although usual 
                         denoising removes discontinuities, which are fundamental for 
                         vector \field analysis. This paper proposes a novel method 
                         for vector \field denoising based on random walks which 
                         preserve those discontinuities. It works in a meshless setting; it 
                         is fast, simple to implement, and shows a better performance than 
                         the traditional gaussian denoising technique.",
  conference-location = "Rio de Janeiro, RJ, Brazil",
      conference-year = "11-14 Oct. 2009",
                  doi = "10.1109/SIBGRAPI.2009.13",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2009.13",
             language = "en",
                  ibi = "8JMKD3MGPBW4/35S5CLP",
                  url = "http://urlib.net/ibi/8JMKD3MGPBW4/35S5CLP",
           targetfile = "57789_2.pdf",
        urlaccessdate = "2024, May 02"
}