@InProceedings{SousaeSantosPedr:2016:ImHuSk,
               author = "Sousa e Santos, Anderson Carlos and Pedrini, H{\'e}lio",
          affiliation = "{University of Campinas} and {University of Campinas}",
                title = "Improvements on human skin segmentation in digital images",
            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 = "segmentation, saliency, texture, skin detection.",
             abstract = "Human skin segmentation has several applications in computer 
                         vision and pattern recognition fields, whose main purpose is to 
                         distinguish skin and non-skin regions. Despite the large number of 
                         available methods, accurate skin segmentation is still a 
                         challenging task. Three main contributions toward this need are 
                         presented in this work. The first is a self-contained method for 
                         adaptive skin segmentation that adjusts the color model to a 
                         particular image. The second is the combination of saliency 
                         detection with color skin segmentation, which performs a 
                         background removal to eliminate non-skin regions. The third is a 
                         texture-based improvement imployed to characterize non-skin 
                         regions and thus eliminates color ambiguity adding a second vote. 
                         Experimental results on public data sets demonstrate a significant 
                         improvement of the proposed methods for human skin segmentation 
                         over state-of-the-art approaches.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M9L6JB",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M9L6JB",
           targetfile = "paper.pdf",
        urlaccessdate = "2024, May 03"
}