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@InProceedings{RodriguesGira:2009:CoQiTs,
               author = "Rodrigues, Paulo Sergio and Giraldi, Gilson Ant{\^o}nio",
          affiliation = "{Centro Universit{\'a}rio da FEI} and {National Laboratory for 
                         Scientific Computing}",
                title = "Computing the q-index for Tsallis Nonextensive Image 
                         Segmentation",
            booktitle = "Proceedings...",
                 year = "2009",
               editor = "Nonato, Luis Gustavo and Scharcanski, Jacob",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 22. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Image Segmentation, Tsallis Entropy.",
             abstract = "The concept of entropy based on Shannon Theory of nformation has 
                         been applied in the \field of image processing and analysis 
                         Since the work of T. Pun. This concept is based on the traditional 
                         Boltzaman-Gibbs entropy, proposed under the classical 
                         thermodynamic. On the other hand, it is well known that this old 
                         formalism fails to explain some physical system if they have 
                         complex behavior such as long rang interactions and long time 
                         memories. Recently, studies in mechanical statistics have proposed 
                         a new kind of entropy, called Tsallis entropy (or non-extensive 
                         entropy), which has been considered with promising results on 
                         several applications in order to explain such phenomena. The main 
                         feature of Tsallis entropy is the q-index parameter, which is 
                         close related to the degree of system nonextensivity. In 2004 was 
                         proposed the \first algorithm for image segmentation based 
                         on Tsallis entropy. However, the computation of the q-index was 
                         already an open problem. On the other hand, in the \field 
                         of image segmentation it is not an easy task to compare the 
                         quality of segmentation results. This is mainly due to the lack of 
                         an image ground truth based on human reasoning. In this paper, we 
                         propose the \first methodology in the \field of 
                         image segmentation for q-index computation and compare it with 
                         other similar approaches using a human based segmentation ground 
                         truth. The results suggest that our approach is a forward step for 
                         image segmentation algorithms based on Information Theory.",
  conference-location = "Rio de Janeiro",
      conference-year = "Oct. 11-14, 2009",
             language = "en",
           targetfile = "PID949547.pdf",
        urlaccessdate = "2020, Dec. 04"
}


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