Multimedia Systems

, Volume 17, Issue 2, pp 113–133 | Cite as

Stabilization and extraction of 2D barcodes for camera phones

  • Chung-Hua Chu
  • De-Nian Yang
  • Ya-Lan Pan
  • Ming-Syan Chen
Original Research

Abstract

With the ubiquity of cellular phones, mobile applications with 2D barcodes have drawn a lot of attentions in recent years. When a user takes a barcode image with the camera in a mobile device, the captured image tends to be blurred due to camera shaking when the user presses the shutter. In addition, the captured image includes part of the complex background of the page with the barcode. In this paper, we point out that the above two issues, which have not been identified in previous works, deteriorate the accuracy of barcode recognition in the mobile computing. We then propose an efficient and effective algorithm to restore and extract 2D barcode from a complex background in a camera-shaken image. Compared with previous approaches, our algorithm outperforms in not only smaller running time but also higher accuracy of the barcode recognition in the mobile computing.

Keywords

2D barcode Image restoration QR code Camera phone 

References

  1. 1.
    Ayatsuka, Y., Rekimoto, J.: Active cybercode: a directly controllable 2d code. In: ACM Special Interest Group on Computer-Human Interaction (2006)Google Scholar
  2. 2.
    Burger, W., Burge, M.J.: Digital Image Processing—An Algorithmic Introduction Using Java, pp. 156–171. Springer, Berlin (2008)Google Scholar
  3. 3.
    Campisi, P., Egiazarian, K.: Blind Image Deconvolution: Theory and Applications, pp. 196–237. CRC Press, Boca Raton (2007)MATHGoogle Scholar
  4. 4.
    Cannon, M.: Blind deconvolution of spatially invariant image blurs with phase. IEEE Trans. Acoust. Speech Signal Process. 24(1), 58–63 (1976)CrossRefGoogle Scholar
  5. 5.
    Chan, R.H., Nagy, J.G., Plemmons, R.J.: Fft-based preconditioners for Toeplitz-block least squares problems. SIAM J. Numer. Anal. 30(6), 1740–1768 (1993)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Chu, C.-H., Yang, D.-N., Chen, M.-S.: Image stabilization for 2d barcodes on camera phones. In: Proceedings of ACM International Conference on Multimedia (2007)Google Scholar
  7. 7.
    Driggers, R.G.: Encyclopedia of Optical Engineering: Abe-las, pp. 412–427. CRC Press, Boca Raton (2003)Google Scholar
  8. 8.
  9. 9.
    Ebrahim, Y., Abdelsalam, W., Ahmed, M., Chau, S.-C.: Proposing a hybrid tag-camera-based identification and navigation aid for the visually impaired. In: IEEE International Conference on Consumer Communications and Networking Conference (2004)Google Scholar
  10. 10.
    Engl, H.W., Hanke, M., Neubauer, A.: Regularization of Inverse Problems. Springer, Berlin (2000)Google Scholar
  11. 11.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice-Hall, Englewood Cliffs (2001)Google Scholar
  12. 12.
    Hadamard, J.: Sur les Problemes aux Derivees Partielles et leur Signification Physique, pp. 49–52. Princeton University Bulletin, NJ (1902)Google Scholar
  13. 13.
    Hadar, O., Rotman, S.R., Kopeika, N.S.: Target acquisition modeling of forward-motion considerations for airborne reconnaissance over hostile territory. Opt. Eng. (SPIE) 33(9) (1994)Google Scholar
  14. 14.
    Hansen, P.C., Nagy, J.G., O’Leary, D.P.: Deblurring Images Matrices, Spectra and Filtering, pp. 71–86. Springer, Berlin (2006)MATHGoogle Scholar
  15. 15.
    Hong, H., Zhang, T.: Fast restoration approach for rotational motion blurred image based on deconvolution along the blurring paths. Photo-Opt. Instrum. Eng. 12(42), 3471–3486 (2003)Google Scholar
  16. 16.
    ISO/IEC 18004:2000: Information technology—automatic identification and data capture techniques—bar code symbology-QR Code (2000)Google Scholar
  17. 17.
    Kamijo, K., Kamijo, N., Sakamoto, M.: Electronic clipping system with invisible barcodes. In: Proceedings of ACM International Conference on Multimedia (2006)Google Scholar
  18. 18.
    Kato, H., Tan, K.-T.: 2d barcodes for mobile phones. In: IEEE International Conference on Mobile Technology, Applications and Systems, p. 8 (2005)Google Scholar
  19. 19.
    Krahmer, F., Lin, Y., McAdoo, B., Ott, K., Wang, J., Widemann, D., Wohlberg, M.B.: Blind image deconvolution: motion blur estimation. In: Mathematical Modeling in Industry (2006)Google Scholar
  20. 20.
    Levin, A.: Blind motion deblurring using image statistics. In: Advances in Neural Information Processing Systems (NIPS) (2006)Google Scholar
  21. 21.
    Li T.-H., Lii, K.-S.: A joint estimation approach for two-tone image deblurring by blind deconvolution. IEEE Trans. Image Process. 11, 847–858 (2002)CrossRefGoogle Scholar
  22. 22.
    Liebgott, H., Wilhjehm, J., Jensen, J., Vray, D., Delachartre, P.: PSF dedicated to estimation of displacement vectors for tissue elasticity imaging with ultrasound. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 54, 746–756 (2007)CrossRefGoogle Scholar
  23. 23.
    Lin, W., Wehrli, F., Song, H.K.: Correcting bulk in-plane motion artifacts in mri using the point spread function. IEEE Trans. Med. Imaging 24, 1170–1176 (2005)CrossRefGoogle Scholar
  24. 24.
    Liu, X., Doermann, D., Li, H.: Vcode expervasive data transfer using video barcode. IEEE Trans. Multimed. 10(3), 361–372 (2008)CrossRefGoogle Scholar
  25. 25.
    Lv, P., Lai, S., Liu, L., Zhuo, Y., Liu, K.: Research and implementation of automatic recognition of two dimensional barcode mobile computing. In: IEEE International Conference on Mobile Technology, Applications and Systems (2005)Google Scholar
  26. 26.
    Mery, D., Filbert, D.: A fast non-iterative algorithm for the removal of blur caused by uniform linear motion in X-ray images. In: 15th World Conference on Nondestructive Testing (2000)Google Scholar
  27. 27.
    Michael, N., Denis, K.: Fundamentals of noise and vibration analysis for engineers, pp. 344–364. Cambridge University Press, London (2003). ISBN 0521499135Google Scholar
  28. 28.
    Moghaddam, M., Jamzad, M.E.: Finding point spread function of motion blur using radon transform and modeling the motion length. In: Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology (2004)Google Scholar
  29. 29.
    Nagy, J.G., Palmer, K.M., Perrone, L.: Iterative methods for image deblurring: a matlab object-oriented approach. Numer. Algorithms 36(1), 73–93 (2004)MATHCrossRefMathSciNetGoogle Scholar
  30. 30.
    Nguyen, N., Milanfar, P.: Efficient generalized cross-validation with applications to parametric image restoration and resolution enhancement. IEEE Trans. Image Process. 10(9), 1299–1308 (2001)MATHCrossRefMathSciNetGoogle Scholar
  31. 31.
    Nixon, M.S., Aguado, A.S.: Feature Extraction and Image Processing, 2nd edn, pp. 195–241. Academic Press, London (2008)Google Scholar
  32. 32.
    Ohbuchi, E., Hanaizumi, H., Hock. L.A.: Barcode readers using the camera device in mobile phones. In: IEEE International Conference on Cyberworlds (CW04) (2004)Google Scholar
  33. 33.
    Parikh, T.S., Lazowska, E.D.: Designing an architecture for delivering mobile information services to the rural developing world. In: Proceedings of ACM International Conference on World Wide Web WWW (2006)Google Scholar
  34. 34.
    Prasad, S.: Statistical-information-based performance criteria for Richardson-Lucy image deblurring. J. Opt. Soc. Am. 19, 1286–1296 (2002)CrossRefGoogle Scholar
  35. 35.
    Proakis, J.G., Manolakis, D.G.: Digital Signal Processing, pp. 261–361. Pearson Prentice Hall, Englewood Cliffs (2007)Google Scholar
  36. 36.
    QR-code encoder and decoder. http://www.psytec.co.jp.
  37. 37.
  38. 38.
    Rafael, R.E.W., Gonzalez, C.: Digital Image Processing, pp. 199–300. Prentice-Hall, Englewood Cliffs (2008)Google Scholar
  39. 39.
    Reaves, S., Mersereau, R.: Optimal regularization parameter estimation for image reconstruction. In: Proceedings of the SPIE Conference on Image Processing Algorithms and Techniques, pp. 127–137 (1991)Google Scholar
  40. 40.
    Reeves, S.J.: Fast image restoration without boundary artifacts. IEEE Trans. Image Process. 14(10), 1448–1453 (2005)CrossRefGoogle Scholar
  41. 41.
    Rekimoto, J., Ayatsuka, Y.: Cybercode: designing augmented reality environments with visual tags. In: Proceedings of ACM International Conference on Designing Augmented Reality Environments (2000)Google Scholar
  42. 42.
    Sondhi, M.M.: Image restoration: the removal of spatially invariant degradations. Proc. IEEE 60(7), 842–853 (1972)CrossRefGoogle Scholar
  43. 43.
    Sorel, M., Flusser, J.: Blind restoration of images blurred by complex camera motion and simultaneous recovery of 3d scene structure. In: IEEE International Symposium on Signal Processing and Information Technology (2005)Google Scholar
  44. 44.
    Tikhonov, A.N.: Solution of incorrectly formulated problems and the regularization method. Sov. Math. Dokl. 1035–1038 (1963)Google Scholar
  45. 45.
    Wax, M., Kailath, T.: Efficient inversion of Toeplitz-block toeplitz matrix. IEEE Trans. Acoust. Speech Signal Process. 31(5), 1218–1221 (1983)MATHCrossRefMathSciNetGoogle Scholar
  46. 46.
  47. 47.
    Yitzhaky, Y., Kopeika, N.S.: Identification of blur parameters from motion blurred images. CVGIP Graph. Models Image Process. 59(5), 321–332 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Chung-Hua Chu
    • 1
  • De-Nian Yang
    • 2
  • Ya-Lan Pan
    • 3
  • Ming-Syan Chen
    • 2
    • 3
  1. 1.Department of Multimedia DesignNational Taichung Institute of TechnologyTaichungTaiwan, ROC
  2. 2.Institute of Information ScienceAcademia SinicaTaipeiTaiwan, ROC
  3. 3.Graduate Institute of Networking and MultimediaNational Taiwan UniversityTaipeiTaiwan, ROC

Personalised recommendations