@InProceedings{SouzaAlvLevCruMar:2016:GrApCo,
               author = "Souza, Gustavo Botelho de and Alves, Gabriel Marcelino and Levada, 
                         Alexandre Lu{\'{\i}}s Magalh{\~a}es and Cruvinel, Paulo 
                         Estev{\~a}o and Marana, Aparecido Nilceu",
          affiliation = "Universidade Federal de S{\~a}o Carlos (UFSCar), Banco do Brasil 
                         and Universidade Federal de S{\~a}o Carlos (UFSCar), Embrapa 
                         Instrumenta{\c{c}}{\~a}o and {Universidade Federal de S{\~a}o 
                         Carlos (UFSCar)} and Embrapa Instrumenta{\c{c}}{\~a}o, 
                         Universidade Federal de S{\~a}o Carlos (UFSCar) and Universidade 
                         Estadual Paulista (UNESP), Universidade Federal de S{\~a}o Carlos 
                         (UFSCar)",
                title = "A Graph-Based Approach for Contextual Image Segmentation",
            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 = "Min Cut-Max Flow, Graph Theory, Anisotropic Diffusion, Image 
                         Segmentation.",
             abstract = "Image segmentation is one of the most important tasks in Image 
                         Analysis since it allows locating the relevant regions of the 
                         images and discarding irrelevant information. Any mistake during 
                         this phase may cause serious problems to the subsequent methods of 
                         the image-based systems. The segmentation process is usually very 
                         complex since most of the images present some kind of noise. In 
                         this work, two techniques are combined to deal with such problem: 
                         one derived from the graph theory and other from the anisotropic 
                         filtering methods, both emphasizing the use of contextual 
                         information in order to classify each pixel in the image with 
                         higher precision. Given a noisy grayscale image, an anisotropic 
                         diffusion filter is applied in order to smooth the interior 
                         regions of the image, eliminating noise without loosing much 
                         information of boundary areas. After that, a graph is built based 
                         on the pixels of the obtained diffused image, linking adjacent 
                         nodes (pixels) and considering the capacity of the edges as a 
                         function of the filter properties. Then, after applying the 
                         Ford-Fulkerson algorithm, the minimum cut of the graph is found 
                         (following the min cut-max flow theorem), segmenting the object of 
                         interest. The results show that the proposed approach outperforms 
                         the traditional and well-referenced Otsu's method.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
                  doi = "10.1109/SIBGRAPI.2016.046",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2016.046",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M469G5",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M469G5",
           targetfile = "PID4357791.pdf",
        urlaccessdate = "2024, May 03"
}