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@InProceedings{MayhuaGomeHeerPoco:2018:ExViEn,
               author = "Mayhua, Angela and Gomez-Nieto, Erick and Heer, Jeffrey and Poco, 
                         Jorge",
          affiliation = "{Universidad Cat{\'o}lica San Pablo} and {Universidad 
                         Cat{\'o}lica San Pablo} and {University of Washington} and 
                         {Funda{\c{c}}{\~a}o Getulio Vargas}",
                title = "Extracting Visual Encodings from Map Chart Images with 
                         Color-encoded Scalar Values",
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "visual encoding, map interpretation, map chart.",
             abstract = "Map charts are used in diverse domains to show geographic data 
                         (e.g., climate research, oceanography, business analysis, etc.). 
                         These charts can be found in news articles, scientific papers, and 
                         on the Web. However, many map charts are available only as bitmap 
                         images, hindering machine interpretation of the visualized data 
                         for indexing and reuse. We propose a pipeline to recover both the 
                         visual encodings and underlying data from bitmap images of 
                         geographic maps with color-encoded scalar values. We evaluate our 
                         results using map images from scientific documents, achieving high 
                         accuracy along each step of our proposal. In addition, we present 
                         two applications: data extraction and map reprojection to enable 
                         improved visual representations of map charts.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "Oct. 29 - Nov. 1, 2018",
             language = "en",
           targetfile = "Paper ID 201.pdf",
        urlaccessdate = "2020, Dec. 04"
}


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