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@InProceedings{BelussiHira:2011:FaQRCo,
               author = "Belussi, Luiz Felipe Franco and Hirata, Nina Sumiko Tomita",
          affiliation = "Institute of Mathematics and Statistics, University of S{\~a}o 
                         Paulo and Institute of Mathematics and Statistics, University of 
                         S{\~a}o Paulo",
                title = "Fast QR code detection in arbitrarily acquired images",
            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 = "QR code, 2D barcode, Haar-like features, cascade classifier, 
                         boosting, classification, pattern recognition.",
             abstract = "The detection of QR codes, a type of 2D barcode, as described in 
                         the literature consists merely in the determination of the 
                         boundaries of the symbol region in images obtained with the 
                         specific intent of highlighting the symbol. However, many 
                         important applications such as those related with accessibility 
                         technologies or robotics, depends on first detecting the presence 
                         of a barcode in an environment. We employ Viola-Jones rapid object 
                         detection framework to address the problem of finding QR codes in 
                         arbitrarily acquired images. This framework provides an efficient 
                         way to focus the detection process in promising regions of the 
                         image and a very fast feature calculation approach for pattern 
                         classification. An extensive study of variations in the parameters 
                         of the framework for detecting finder patterns, present in three 
                         corners of every QR code, was carried out. Detection accuracy 
                         superior to 90%, with controlled number of false positives, is 
                         achieved. We also propose a post-processing algorithm that 
                         aggregates the results of the first step and decides if the 
                         detected finder patterns are part of QR code symbols. This 
                         two-step processing is done in real time.",
  conference-location = "Macei{\'o}",
      conference-year = "Aug. 28 - 31, 2011",
             language = "en",
           targetfile = "86622_final.pdf",
        urlaccessdate = "2019, Dec. 07"
}


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