Computing Range Flow from Multi-modal Kinect Data
Conference paper
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.
Keywords
Color Image Depth Image Gesture Recognition Color Channel Depth Channel
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.
References
- 1.Web page with used data and experiments of this paper, http://hci.iwr.uni-heidelberg.de/Staff/jgottfri/papers/flowKinect.php
- 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.Besl, P.J.: Active, optical range imaging sensors. Machine vision and applications 1(2), 127–152 (1988)CrossRefGoogle Scholar
- 4.Burrus, N.: Kinect calibration - calibrating the depth and color camera, http://nicolas.burrus.name/index.php/Research/KinectCalibration
- 5.Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artif. Intell. 17(1-3), 185–203 (1981)CrossRefGoogle Scholar
- 6.Martin, H.: Openkinect project - drivers and libraries for the xbox kinect device, http://openkinect.org
- 7.Opencv (open source computer vision) - a library of programming functions for real time computer vision, http://opencv.willowgarage.com
- 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.Salgado, A., Sánchez, J.: A temporal regularizer for large optical flow estimation. In: ICIP, pp. 1233–1236. IEEE, Los Alamitos (2006)Google Scholar
- 10.Scharr, H.: Optimale Operatoren in der digitalen Bildverarbeitung. Ph.D. thesis, Universität Heidelberg (2000)Google Scholar
- 11.Spies, H., Jähne, B., Barron, J.L.: Range flow estimation. Computer Vision and Image Understanding 85(3), 209–231 (2002)CrossRefzbMATHGoogle Scholar
- 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.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.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