Centrality bias measure for high density QR code module recognition

Iuliia Tkachenko 1 William Puech 1 Olivier Strauss 1 Jean-Marc Gaudin 2 Christophe Destruel 2 Christian Guichard 2
1 ICAR - Image & Interaction
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : High density bar codes are very popular today due to large storage capacity and small code area. But unfortunately, high density versions of QR codes are not being used due to reading problems with most smartphones and flatbed scanner QR code applications. Due to frequent changes and small sizes of the black and white modules, there are reading problems in the QR code binarization and tilt correction processes. The binarization method sets the global or local threshold, and binarizes each pixel separately, that is why they are sensitive to print-and-scan distortion and luminosity. In this paper, we focus on the recognition of high density QR codes. We propose to use the centrality bias of each module to improve the module recognition results. This measure has been used for proposed classification methods and for standard QR code recognition methods. In both cases, the recognition rate was improved, as confirmed by the experimental results.
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Article dans une revue
Signal Processing: Image Communication, Elsevier, 2016, 41, pp.46-60. 〈10.1016/j.image.2015.11.007〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01337364
Contributeur : Isabelle Gouat <>
Soumis le : samedi 25 juin 2016 - 14:06:13
Dernière modification le : jeudi 11 janvier 2018 - 06:26:18

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Iuliia Tkachenko, William Puech, Olivier Strauss, Jean-Marc Gaudin, Christophe Destruel, et al.. Centrality bias measure for high density QR code module recognition. Signal Processing: Image Communication, Elsevier, 2016, 41, pp.46-60. 〈10.1016/j.image.2015.11.007〉. 〈lirmm-01337364〉

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