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@InProceedings{HonórioFoSiAlMaCaRoRe:2018:ApNaOu,
               author = "Hon{\'o}rio Filho, Paulo and Silva, Suane Pires P. da and 
                         Almeida, Jefferson S. and Marinho, Leandro B. and Carneiro, Tiago 
                         and Rodrigues, Antonio Wendell de O. and Rebou{\c{c}}as Filho, 
                         Pedro Pedrosa",
          affiliation = "Programa de P{\'o}s-Gradua{\c{c}}{\~a}o em Ci{\^e}ncia da 
                         Computa{\c{c}}{\~a}o (PPGCC), Instituto Federal do Cear{\'a}, 
                         Fortaleza, Cear{\'a}, Brazil and Programa de 
                         P{\'o}s-Gradua{\c{c}}{\~a}o em Ci{\^e}ncia da 
                         Computa{\c{c}}{\~a}o (PPGCC), Instituto Federal do Cear{\'a}, 
                         Fortaleza, Cear{\'a}, Brazil and Programa de 
                         P{\'o}s-Gradua{\c{c}}{\~a}o em Ci{\^e}ncia da 
                         Computa{\c{c}}{\~a}o (PPGCC), Instituto Federal do Cear{\'a}, 
                         Fortaleza, Cear{\'a}, Brazil and Programa de 
                         P{\'o}s-Gradua{\c{c}}{\~a}o em Engenharia de 
                         Teleinform{\'a}tica (PPGETI), Universidade Federal do Cear{\'a}, 
                         Fortaleza, Cear{\'a}, Brazil and Programa de 
                         P{\'o}s-Gradua{\c{c}}{\~a}o em Ci{\^e}ncia da 
                         Computa{\c{c}}{\~a}o (PPGCC), Instituto Federal do Cear{\'a}, 
                         Fortaleza, Cear{\'a}, Brazil and Programa de 
                         P{\'o}s-Gradua{\c{c}}{\~a}o em Ci{\^e}ncia da 
                         Computa{\c{c}}{\~a}o (PPGCC), Instituto Federal do Cear{\'a}, 
                         Fortaleza, Cear{\'a}, Brazil and Programa de 
                         P{\'o}s-Gradua{\c{c}}{\~a}o em Ci{\^e}ncia da 
                         Computa{\c{c}}{\~a}o (PPGCC), Instituto Federal do Cear{\'a}, 
                         Fortaleza, Cear{\'a}, Brazil",
                title = "An Approach to Navigation in Outdoor and Indoor Environments with 
                         Unmanned Aerial Vehicle Using Visual Topological Map",
            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 = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Unmanned Aerial Vehicles, Computer Vision, Topological Maps, UAV 
                         Navigation.",
             abstract = "Unmanned Aerial Vehicles (UAVs) are increasingly being applied in 
                         professional activities that require higher precision in 
                         navigating and positioning the aircraft in flight. Advanced 
                         location technologies such as GNSS (Global Navigation Satellite 
                         System) and RTK (Real-Time Kinematic), can raise the cost of 
                         demand using UAVs or still be dependent on an area with a 
                         transmission coverage. In this context, this article presents a 
                         visual navigation methodology based on topological maps comparing 
                         the performance of consolidated classifiers such as Bayesian 
                         classifier, k-Nearest Neighbor (kNN), Multi-layer Perceptron 
                         (MLP), Optimum-Path Forest (OPF) and Support Vector Machines 
                         (SVM), using attributes returned by state-of-the-art feature 
                         extractors such as Fourier, Gray Level Co-Occurrence (GLCM) and 
                         Local Binary Patterns (LBP). The results show that the combination 
                         of LBP with SVM obtained the best values in the evaluation metrics 
                         considered, among them, 99.99% of Specificity and 99.98% of 
                         Accuracy in the navigation process.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "Oct. 29 - Nov. 1, 2018",
             language = "en",
                  ibi = "8JMKD3MGPAW/3S3PUPL",
                  url = "http://urlib.net/rep/8JMKD3MGPAW/3S3PUPL",
           targetfile = "paulo_sibgrapi.pdf",
        urlaccessdate = "2020, Nov. 29"
}


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