@InProceedings{LieBorbVieiGamb:2016:FaSaDe,
               author = "Lie, Maiko Min Ian and Borba, Gustavo Benvenutti and Vieira Neto, 
                         Hugo and Gamba, Humberto Remigio",
          affiliation = "Federal University of Technology - Parana, Graduate Program in 
                         Electrical and Computer Engineering and Federal University of 
                         Technology - Parana, Graduate Program in Biomedical Engineering 
                         and Federal University of Technology - Parana, Graduate Program in 
                         Electrical and Computer Engineering and Federal University of 
                         Technology - Parana, Graduate Program in Electrical and Computer 
                         Engineering",
                title = "Fast Saliency Detection Using Sparse Random Color Samples and 
                         Joint Upsampling",
            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 = "IEEE Computer Society´s Conference Publishing Services",
              address = "Los Alamitos",
             keywords = "saliency detection, Fast Global Smoother, joint upsampling, visual 
                         attention.",
             abstract = "The human visual system employs a mechanism of visual attention, 
                         which selects only part of the incoming information for further 
                         processing. Through this mechanism, the brain avoids overloading 
                         its limited cognitive capacities. In computer vision, this task is 
                         usually accomplished through saliency detection, which outputs the 
                         regions of an image that are distinctive with respect to its 
                         surroundings. This ability is desirable in many technological 
                         applications, such as image compression, video quality assessment 
                         and content-based image retrieval. In this paper, a saliency 
                         detection method based on color distance with sparse random 
                         samples and joint upsampling is presented. This approach computes 
                         full-resolution saliency maps with short runtime by leveraging 
                         both edge-preserving smoothing and joint upsampling capabilities 
                         of the Fast Global Smoother. The proposed method is assessed 
                         through precision-recall curves, F-measure and average runtime on 
                         the MSRA1K dataset. Results show that the method is competitive 
                         with state-of-the-art algorithms in both saliency detection 
                         accuracy and runtime.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
                  doi = "10.1109/SIBGRAPI.2016.038",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2016.038",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M3CK42",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M3CK42",
           targetfile = "PID4348243.pdf",
        urlaccessdate = "2024, May 03"
}