QR Code Localization Using Boosted Cascade of Weak Classifiers

  • Péter BodnárEmail author
  • László G. Nyúl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8814)


Usage of computer-readable visual codes became common in our everyday life at industrial environments and private use. The reading process of visual codes consists of two steps: localization and data decoding. Unsupervised localization is desirable at industrial setups and for visually impaired people. This paper examines localization efficiency of cascade classifiers using Haar-like features, Local Binary Patterns and Histograms of Oriented Gradients, trained for the finder patterns of QR codes and for the whole code region as well, and proposes improvements in post-processing.


QR code Object detection Cascade classifier HAAR LBP HOG 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Parikh, D., Jancke, G.: Localization and segmentation of a 2D high capacity color barcode. In: IEEE Workshop on Applications of Computer Vision, WACV 2008, pp. 1–6 (2008)Google Scholar
  2. 2.
    Chu, C.H., Yang, D.N., Pan, Y.L., Chen, M.S.: Stabilization and extraction of 2D barcodes for camera phones. Multimedia Systems 17, 113–133 (2011)CrossRefGoogle Scholar
  3. 3.
    Ohbuchi, E., Hanaizumi, H., Hock, L.A.: Barcode readers using the camera device in mobile phones. In: 2004 International Conference on Cyberworlds, pp. 260–265 (2004)Google Scholar
  4. 4.
    Belussi, L.F.F., Hirata, N.S.T.: Fast QR code detection in arbitrarily acquired images. In: 2011 24th SIBGRAPI Conference on Graphics, Patterns and Images (Sibgrapi), pp. 281–288 (2011)Google Scholar
  5. 5.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, pp. I-511–I-518 (2001)Google Scholar
  6. 6.
    Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical analysis of detection cascades of boosted classifiers for rapid object detection. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 297–304. Springer, Heidelberg (2003) CrossRefGoogle Scholar
  7. 7.
    Bodnár, P., Nyúl, L.G.: A novel method for barcode localization in image domain. In: Kamel, M., Campilho, A. (eds.) ICIAR 2013. LNCS, vol. 7950, pp. 189–196. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  8. 8.
    Ojala, T., Pietikainen, M., Harwood, D.: Performance evaluation of texture measures with classification based on kullback discrimination of distributions. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition, Conference A: Computer Vision and Image Processing, vol. 1, pp. 582–585 (1994)Google Scholar
  9. 9.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1 pp. 886–893 (2005)Google Scholar
  10. 10.
    Wang, X., Han, T., Yan, S.: An HOG-LBP human detector with partial occlusion handling. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 32–39 (2009)Google Scholar
  11. 11.
    Sörös, G., Flörkemeier, C.: Blur-resistant joint 1D and 2D barcode localization for smartphones. In: Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia, MUM 2013, pp. 11:1–11:8. ACM, New York (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Image Processing and Computer GraphicsUniversity of SzegedSzegedHungary

Personalised recommendations