@InCollection{KortingCastFons:2013:DiSeMe,
               author = "Korting, Thales Sehn and Castejon, Emiliano Ferreira and Fonseca, 
                         Leila Maria Garcia",
                title = "The Divide and Segment Method for Parallel Image Segmentation",
            booktitle = "Advanced concepts for intelligen vision systems",
            publisher = "Springer",
                 year = "2013",
               editor = "Blanc-Talon, J. and Kasinski, A. and Philips, W. and Popescu, D. 
                         and Sheunders, P.",
                pages = "504--515",
              address = "Berlin",
             keywords = "parallel image segmentation, remote sensing, large spatial 
                         dimensions.",
             abstract = "Remote sensing images with large spatial dimensions are usual. 
                         Besides, they also include a diversity of spectral channels, 
                         increasing the volume of information. To obtain valuable 
                         information from remote sensing data, computers need higher 
                         amounts of memory and more efficient processing techniques. The 
                         first process in image analysis is segmentation, which identifies 
                         regions in images. Therefore, segmentation algorithms must deal 
                         with large amounts of data. Even with current computational power, 
                         certain image sizes may exceed the memory limits, which ask for 
                         different solutions. An alternative to overcome such limits is to 
                         employ the well-known divide and conquer strategy, by splitting 
                         the image into chunks, and segmenting each one individually. 
                         However, it arises the problem of merging neighboring chunks and 
                         keeping the homogeneity in such regions. In this work, we propose 
                         an alternative to divide the image into chunks by defining 
                         noncrisp borders between them. The noncrisp borders are computed 
                         based on Dijkstra algorithm, which is employed to find the 
                         shortest path between detected edges in the images. By applying 
                         our method, we avoid the postprocessing of neighboring regions, 
                         and therefore speed up the final segmentation.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                  doi = "10.1007/978-3-319-02895-8_45",
                  url = "http://dx.doi.org/10.1007/978-3-319-02895-8_45",
                 isbn = "9783319028941",
                label = "lattes: 5123287769635741 3 K{\"o}rtingCastFons:2013:DiSeMe",
             language = "pt",
           targetfile = "korting2013divide.pdf",
                  url = "http://link.springer.com/10.1007/978-3-319-02895-8_45",
        urlaccessdate = "2024, Apr. 29"
}