Barcode Imaging Using a Light Field Camera

  • Xinqing GuoEmail author
  • Haiting Lin
  • Zhan Yu
  • Scott McCloskey
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8926)


We present a method to capture sharp barcode images, using a microlens-based light field camera. Relative to standard barcode readers, which typically use fixed-focus cameras in order to reduce mechanical complexity and shutter lag, employing a light field camera significantly increases the scanner’s depth of field. However, the increased computational complexity that comes with software-based focusing is a major limitation on these approaches. Whereas traditional light field rendering involves time-consuming steps intended to produce a focus stack in which all objects appear sharply-focused, a scanner only needs to produce an image of the barcode region that falls within the decoder’s inherent robustness to defocus. With this in mind, we speed up image processing by segmenting the barcode region before refocus is applied. We then estimate the barcode’s depth directly from the raw sensor image, using a lookup table characterizing a relationship between depth and the code’s spatial frequency. Real image experiments with the Lytro camera illustrate that our system can produce a decodable image with a fraction of the computational complexity.


Light field camera Barcode imaging Spatial frequency 


  1. 1.
    Chai, D., Hock, F.: Locating and decoding ean-13 barcodes from images captured by digital cameras. In: 2005 Fifth International Conference on Information, Communications and Signal Processing, pp. 1595–1599 (2005)Google Scholar
  2. 2.
    Dansereau, D., Bruton, L.: Gradient-based depth estimation from 4d light fields. In: Proceedings of the 2004 International Symposium on Circuits and Systems ISCAS 2004, vol. 3, pp. 549–552 (2004)Google Scholar
  3. 3.
    Dansereau, D., Pizarro, O., Williams, S.: Decoding, calibration and rectification for lenselet-based plenoptic cameras. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1027–1034 (2013)Google Scholar
  4. 4.
    Fiss, J., Curless, B., Szeliski, R.: Refocusing plenoptic images using depth-adaptive splatting. In: International Conference on Computational Photography (ICCP 2014). IEEE Computer Society (2014)Google Scholar
  5. 5.
    Gallo, O., Manduchi, R.: Reading 1d barcodes with mobile phones using deformable templates. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(9), 1834–1843 (2011)CrossRefGoogle Scholar
  6. 6.
    Georgiev, T., Yu, Z., Lumsdaine, A., Goma, S.: Lytro camera technology: theory, algorithms, performance analysis. In: Proc. SPIE 8667 (2013)Google Scholar
  7. 7.
    Gortler, S.J., Grzeszczuk, R., Szeliski, R., Cohen, M.F.: The lumigraph. In: SIGGRAPH 1996, pp. 43–54 (1996)Google Scholar
  8. 8.
    Levoy, M., Hanrahan, P.: Light field rendering. In: SIGGRAPH 1996, pp. 31–42 (1996)Google Scholar
  9. 9.
    Muniz, R., Junco, L., Otero, A.: A robust software barcode reader using the hough transform. In: Proceedings of the 1999 International Conference on Information Intelligence and Systems, pp. 313–319 (1999)Google Scholar
  10. 10.
    Ng, R., Levoy, M., Bredif, M., Duval, G., Horowitz, M., Hanrahan, P.: Light field photography with a hand-held plenoptic camera. Stanford University Computer Science Tech. Report 2, 1–11 (2005)Google Scholar
  11. 11.
    Tao, M.W., Hadap, S., Malik, J., Ramamoorthi, R.: Depth from combining defocus and correspondence using light-field cameras. In: Proceedings of the 2013 IEEE International Conference on Computer Vision, pp. 673–680 (2013)Google Scholar
  12. 12.
    Vaish, V., Wilburn, B., Joshi, N., Levoy, M.: Using plane + parallax for calibrating dense camera arrays. In: 2004 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2–9 (2004)Google Scholar
  13. 13.
    Wanner, S., Goldluecke, B.: Variational light field analysis for disparity estimation and super-resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(3), 606–619 (2014)CrossRefGoogle Scholar
  14. 14.
    Xu, W., McCloskey, S.: 2d barcode localization and motion deblurring using a flutter shutter camera. In: 2011 IEEE Workshop on Applications of Computer Vision (WACV), pp. 159–165 (2011)Google Scholar
  15. 15.
    Yu, Z., Guo, X., Ling, H., Lumsdaine, A., Yu, J.: Line assisted light field triangulation and stereo matching. In: Proceedings of the 2013 IEEE International Conference on Computer Vision, pp. 2792–2799. ICCV 2013 (2013)Google Scholar
  16. 16.
    Zhang, C., Wang, J., Han, S., Yi, M., Zhang, Z.: Automatic real-time barcode localization in complex scenes. In: 2006 IEEE International Conference on Image Processing, pp. 497–500 (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Xinqing Guo
    • 1
    Email author
  • Haiting Lin
    • 1
  • Zhan Yu
    • 1
  • Scott McCloskey
    • 2
  1. 1.University of DelawareNewarkUSA
  2. 2.Honeywell ACS LabsMinneapolisUSA

Personalised recommendations