Computing Range Flow from Multi-modal Kinect Data

  • Jens-Malte Gottfried
  • Janis Fehr
  • Christoph S. Garbe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6938)

Abstract

In this paper, we present a framework for range flow estimation from Microsoft’s multi-modal imaging device Kinect. We address all essential stages of the flow computation process, starting from the calibration of the Kinect, over the alignment of the range and color channels, to the introduction of a novel multi-modal range flow algorithm which is robust against typical (technology dependent) range estimation artifacts.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Web page with used data and experiments of this paper, http://hci.iwr.uni-heidelberg.de/Staff/jgottfri/papers/flowKinect.php
  2. 2.
    Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. In: ICCV, pp. 1–8. IEEE, Los Alamitos (2007), http://vision.middlebury.edu/flow Google Scholar
  3. 3.
    Besl, P.J.: Active, optical range imaging sensors. Machine vision and applications 1(2), 127–152 (1988)CrossRefGoogle Scholar
  4. 4.
    Burrus, N.: Kinect calibration - calibrating the depth and color camera, http://nicolas.burrus.name/index.php/Research/KinectCalibration
  5. 5.
    Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artif. Intell. 17(1-3), 185–203 (1981)CrossRefGoogle Scholar
  6. 6.
    Martin, H.: Openkinect project - drivers and libraries for the xbox kinect device, http://openkinect.org
  7. 7.
    Opencv (open source computer vision) - a library of programming functions for real time computer vision, http://opencv.willowgarage.com
  8. 8.
    Papenberg, N., Bruhn, A., Brox, T., Didas, S., Weickert, J.: Highly accurate optic flow computation with theoretically justified warping. International Journal of Computer Vision 67(2), 141–158 (2006)CrossRefGoogle Scholar
  9. 9.
    Salgado, A., Sánchez, J.: A temporal regularizer for large optical flow estimation. In: ICIP, pp. 1233–1236. IEEE, Los Alamitos (2006)Google Scholar
  10. 10.
    Scharr, H.: Optimale Operatoren in der digitalen Bildverarbeitung. Ph.D. thesis, Universität Heidelberg (2000)Google Scholar
  11. 11.
    Spies, H., Jähne, B., Barron, J.L.: Range flow estimation. Computer Vision and Image Understanding 85(3), 209–231 (2002)CrossRefMATHGoogle Scholar
  12. 12.
    Sun, D., Roth, S., Black, M.J.: Secrets of optical flow estimation and their principles. In: CVPR, pp. 2432–2439. IEEE, Los Alamitos (2010)Google Scholar
  13. 13.
    Sun, D., Roth, S., Lewis, J.P., Black, M.J.: Learning optical flow. In: Forsyth, D.A., Torr, P.H.S., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 83–97. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    Vedula, S., Baker, S., Rander, P., Collins, R.T., Kanade, T.: Three-dimensional scene flow. In: ICCV, pp. 722–729 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jens-Malte Gottfried
    • 1
    • 3
  • Janis Fehr
    • 1
    • 3
  • Christoph S. Garbe
    • 2
    • 3
  1. 1.Heidelberg Collaboratory for Image Processing (HCI)University of HeidelbergGermany
  2. 2.Interdisciplinary Center for Scientific Computing (IWR)University of HeidelbergGermany
  3. 3.Intel Visual Computing Institute (IVCI)Saarland UniversitySaarbrückenGermany

Personalised recommendations