@InProceedings{HappFeitBentFari:2012:PaImSe,
               author = "Happ, Patrick and Feitosa, Raul and Bentes, Cristiana and Farias, 
                         Ricardo",
                title = "A parallel image segmentation algorithm on GPUS",
            booktitle = "Proceedings...",
                 year = "2012",
               editor = "Feitosa, Raul Queiroz and Costa, Gilson Alexandre Ostwald Pedro da 
                         and Almeida, Cl{\'a}udia Maria de and Fonseca, Leila Maria Garcia 
                         and Kux, Hermann Johann Heinrich",
                pages = "580--585",
         organization = "International Conference on Geographic Object-Based Image 
                         Analysis, 4. (GEOBIA).",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "Image Segmentation, Parallel Processing, GPU.",
             abstract = "Image segmentation is a computationally expensive task that 
                         continuously presents performance challenges due to the increasing 
                         volume of available high resolution remote sensing images. 
                         Nowadays, Graphics Processing Units (GPUs) are emerging as an 
                         attractive computing platform for general purpose computations due 
                         to their extremely high floating-point processing performance and 
                         their comparatively low cost. In the image analysis context, the 
                         use of GPUs can accelerate the segmentation process. This work 
                         presents a parallel implementation of a region growing algorithm 
                         for GPUs. The parallel algorithm is based on processing each pixel 
                         as a different thread so as to take advantage of the fine-grain 
                         parallel capability of the GPU. In addition to the parallel 
                         algorithm, the paper also suggests a modification to the 
                         heterogeneity computation that improves the segmentation 
                         performance. The experiments results demonstrate that the parallel 
                         algorithm achieve significant performance gains, running up to 6.8 
                         times faster than the sequential approach.",
  conference-location = "Rio de Janeiro",
      conference-year = "May 7-9, 2012",
                 isbn = "978-85-17-00059-1",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP8W/3BTFE7P",
                  url = "http://urlib.net/ibi/8JMKD3MGP8W/3BTFE7P",
           targetfile = "162.pdf",
                 type = "Segmentation",
        urlaccessdate = "2024, Apr. 29"
}