@InProceedings{VasconcelosCampNasc:2016:KeDeBa,
               author = "Vasconcelos, Levi Osterno and Campos, Mario Fernandes Montenegro 
                         and Nascimento, Erickson Rangel do",
          affiliation = "{Universidade Federal de Minas Gerais} and {Universidade Federal 
                         de Minas Gerais} and {Universidade Federal de Minas Gerais}",
                title = "A Keypoint detector based on Visual and Depth features",
            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 = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "keypoint detector, RGB-D image, decision tree, information 
                         fusion.",
             abstract = "One of the first steps in numerous computer vision tasks is the 
                         extraction of keypoints. Despite the large number of works 
                         proposing image keypoint detectors, only a few methodologies are 
                         able to efficiently use both visual and geometrical information. 
                         In this work we introduce KVD (Keypoints from Visual and Depth 
                         Data), a novel keypoint detector which is scale invariant and 
                         combines intensity and geometrical data using a decision tree. We 
                         present results from several experiments showing that our 
                         methodology produces the best performing detector when compared to 
                         state-of-the-art methods, with the highest repeatability scores 
                         for rotations, translations and scale changes, as well as 
                         robustness to corrupted visual or geometric data. Additionally, as 
                         processing time is concerned, KVD yields the best time performance 
                         among methods that also use depth and visual data.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M9KS2S",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M9KS2S",
           targetfile = "camera-ready-levi.pdf",
        urlaccessdate = "2024, May 03"
}