Fast PDE-Based Image Analysis in Your Pocket

  • Andreas Luxenburger
  • Henning Zimmer
  • Pascal Gwosdek
  • Joachim Weickert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6667)

Abstract

The increasing computing power of modern smartphones opens the door for interesting mobile image analysis applications. In this paper, we explore the arising possibilities but also discuss remaining challenges by implementing linear and nonlinear diffusion filters as well as basic variational optic flow approaches on a modern Android smartphone. To achieve low runtimes, we present a fast method for acquiring images from the built-in camera and focus on efficient solution strategies for the arising partial differential equations (PDEs): Linear diffusion is realised by approximating a Gaussian convolution by means of an iterated box filter. For nonlinear diffusion and optic flow estimation we use the recent fast explicit diffusion (FED) solver. Our experiments on a recent smartphone show that linear/nonlinear diffusion filters can be applied in realtime/near-realtime to images of size 176×144. Computing optic flow fields of a similar resolution requires some seconds, while achieving a reasonable quality.

Keywords

Nonlinear Diffusion Linear Diffusion High Dynamic Range Imaging Android Platform Image Gallery 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Perona, P., Malik, J.: Scale space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 629–639 (1990)CrossRefGoogle Scholar
  2. 2.
    Bruhn, A., Weickert, J., Schnörr, C.: Lucas/Kanade meets Horn/Schunck: Combining local and global optic flow methods. International Journal of Computer Vision 61, 211–231 (2005)CrossRefGoogle Scholar
  3. 3.
    Horn, B., Schunck, B.: Determining optical flow. Artificial Intelligence 17, 185–203 (1981)CrossRefGoogle Scholar
  4. 4.
    Wells, W.M.: Efficient synthesis of Gaussian filters by cascaded uniform filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 8, 234–239 (1986)CrossRefGoogle Scholar
  5. 5.
    Grewenig, S., Weickert, J., Bruhn, A.: From box filtering to fast explicit diffusion. In: Goesele, M., Roth, S., Kuijper, A., Schiele, B., Schindler, K. (eds.) DAGM 2010. LNCS, vol. 6376, pp. 533–542. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Wells, M.T.: Mobile image processing on the Google phone with the Android operating system (2009), http://www.3programmers.com/mwells/documents/pdf/Final (retrieved 2011-01-06)
  7. 7.
    GMA3: Moon filter (2010), http://itunes.apple.com/en/app/moon-filter/id387317833, (retrieved 2011-01-06)
  8. 8.
    Gogolok, R., Steinel, A.: Shockmypic (2009), http://www.shockmypic.com/iphone/ (retrieved 2011-01-06)
  9. 9.
    Bradski, G., Kaehler, A.: Learning OpenCV: computer vision with the OpenCV library. O’Reilly, Sebastopol (2008)Google Scholar
  10. 10.
    Bouguet, J.Y.: Pyramidal implementation of the Lucas Kanade feature tracker – description of the algorithm (2000), http://trac.assembla.com/dilz_mgr/export/272/doc/ktl-tracking/algo_tracking.pdf (retrieved 2011-01-06)
  11. 11.
    Harmat, A.: Variational optic flow (2010), http://sourceforge.net/projects/varflow/ (retrieved 2011-01-06)
  12. 12.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Bischof, H., Leonardis, A., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Olsson, S., Åkesson, P.: Distributed mobile computer vision and applications on the Android platform. Master’s thesis, Faculty of Engineering, Lund University, Sweden (2009)Google Scholar
  14. 14.
    Ballagas, R., Rohs, M., Sheridan, J.G.: Mobile phones as pointing devices. In: Rukzio, E., Hakkila, J., Spasojevic, M., Mäntyjärvi, J. (eds.) Proc. 2005 Pervasive Mobile Interaction Devices, Munich, Germany, vol. 6, pp. 1–4 (2005)Google Scholar
  15. 15.
    Wagner, D., Mulloni, A., Langlotz, T., Schmalstieg, D.: Real-time panoramic mapping and tracking on mobile phones. In: Lok, B., Klinker, G., Nakatsu, R. (eds.) Proc. IEEE Virtual Reality Conference 2010, Waltham, MA, pp. 211–218 (2010)Google Scholar
  16. 16.
    Wagner, D., Schmalstieg, D., Bischof, H.: Multiple target detection and tracking with guaranteed framerates on mobile phones. In: Proc. of IEEE Int. Symposium on Mixed and Augmented Reality 2009, Orlando, FL (2009)Google Scholar
  17. 17.
    Iijima, T.: Basic theory of pattern observation. In: Papers of Technical Group on Automata and Automatic Control. IECE, Japan (1959) (in Japanese)Google Scholar
  18. 18.
    Catté, F., Lions, P.L., Morel, J.M., Coll, T.: Image selective smoothing and edge detection by nonlinear diffusion. SIAM Journal on Numerical Analysis 32, 1895–1909 (1992)CrossRefMATHMathSciNetGoogle Scholar
  19. 19.
    Weickert, J.: Anisotropic Diffusion in Image Processing. Teubner, Stuttgart (1998)MATHGoogle Scholar
  20. 20.
    Liang, S.: Java Native Interface: Programmer’s Guide and Reference. Addison–Wesley, Boston (1999)Google Scholar
  21. 21.
    Meier, R.: Professional Android 2 Application Development. Wrox Press Ltd., Birmingham (2010)Google Scholar
  22. 22.
    Dupuis, E.: Optimizing YUV–RGB color space conversion using Intel’s SIMD technology (2003), http://lestourtereaux.free.fr/papers/data/yuvrgb.pdf, (retrieved 2011-01-07)

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Andreas Luxenburger
    • 1
  • Henning Zimmer
    • 1
  • Pascal Gwosdek
    • 1
  • Joachim Weickert
    • 1
  1. 1.Mathematical Image Analysis Group, Faculty of Mathematics and Computer ScienceSaarland UniversitySaarbrückenGermany

Personalised recommendations