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@InProceedings{LaraHira:2011:CoFeCl,
               author = "Lara, Arnaldo C{\^a}mara and Hirata J{\'u}nior, Roberto",
          affiliation = "{Instituto de Matem{\'a}tica e Estat{\'{\i}}stica - 
                         Universidade de S{\~a}o Paulo} and {Instituto de Matem{\'a}tica 
                         e Estat{\'{\i}}stica - Universidade de S{\~a}o Paulo}",
                title = "Combining features to a class-specific model in an instance 
                         detection framework",
            booktitle = "Proceedings...",
                 year = "2011",
               editor = "Lewiner, Thomas and Torres, Ricardo",
         organization = "Conference on Graphics, Patterns and Images, 24. (SIBGRAPI)",
            publisher = "IEEE Computer Society Conference Publishing Services",
              address = "Los Alamitos",
             keywords = "instance classification, combining features, object model.",
             abstract = "Object detection is a Computer Vision task that determines if 
                         there is an object of some category (class) in an image or video 
                         sequence. When the classes are formed by only one specific object, 
                         person or place, the task is known as instance detection. Object 
                         recognition classifies an object as belonging to a class in a set 
                         of known classes. In this work we deal with an instance 
                         detection/recognition task. We collected pictures of famous 
                         landmarks from the Internet to build the instance classes and test 
                         our framework. Some examples of the classes are: monuments, 
                         churches, ancient constructions or modern buildings. We tested 
                         several approaches to the problem and a new global feature is 
                         proposed to be combined to some widely known features like PHOW. A 
                         combination of features and classifiers to model the given 
                         instances in the training phase was the most successful one.",
  conference-location = "Macei{\'o}",
      conference-year = "Aug. 28 - 31, 2011",
             language = "en",
           targetfile = "86781.pdf",
        urlaccessdate = "2019, Dec. 07"
}


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