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@InProceedings{PereiraGomeCarv:2010:NoApSu,
               author = "Pereira, Eanes Torres and Gomes, Herman Martins and Carvalho, 
                         Jo{\~a}o Marques de",
          affiliation = "{Departamento de Sistemas e Computa{\c{c}}{\~a}o - Universidade 
                         Federal de Campina Grande} and {Departamento de Sistemas e 
                         Computa{\c{c}}{\~a}o - Universidade Federal de Campina Grande} 
                         and {Departamento de Engenharia El{\'e}trica - Universidade 
                         Federal de Campina Grande}",
                title = "Integral Local Binary Patterns: a Novel Approach Suitable for 
                         Texture-Based Object Detection Tasks",
            booktitle = "Proceedings...",
                 year = "2010",
               editor = "Bellon, Olga and Esperan{\c{c}}a, Claudio",
         organization = "Conference on Graphics, Patterns and Images, 23. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Integral Histograms, Local Binary Patterns, Integral LBP, Face 
                         Detection.",
             abstract = "This work is concerned with the proposition and empirical 
                         evaluation of a new feature extraction approach that combines two 
                         existing image descriptors, Integral Histograms and Local Binary 
                         Patterns (LBP), to achieve a representation that exhibits relevant 
                         properties to object detection tasks (such as face detection): 
                         fast constant time processing, rotation, and scale invariance. 
                         This novel approach is called the Integral Local Binary Patterns 
                         (INTLBP), which is based on an existing method for calculating 
                         Integral Histograms from LBP images. This paper empirically 
                         demonstrates the properties of INTLBP in a scenario of texture 
                         extraction for face/non-face classification. Experiments have 
                         shown that the new representation added robustness to scale 
                         variations in the test images - the proposed approach achieved a 
                         mean classification rate 92% higher than the standard Rotation 
                         Invariant LBP approach, when testing over images with scales 
                         different from the ones used for training. Moreover, the INTLBP 
                         dramatically reduced the required processing time when searching 
                         patterns in a face detection task.",
  conference-location = "Gramado",
      conference-year = "Aug. 30 - Sep. 3, 2010",
             language = "en",
           targetfile = "70827_2.pdf",
        urlaccessdate = "2020, Nov. 24"
}


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