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Flocks of Features for Tracking Articulated Objects

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Real-Time Vision for Human-Computer Interaction

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References

  1. S Birchfield. Elliptical head tracking using intensity gradients and color histograms. Proc CVPR, 1998.

    Google Scholar 

  2. G R Bradski. Real-time face and object tracking as a component of a perceptual user interface. Proc IEEE Workshop on Applications of Computer Vision, 1998.

    Google Scholar 

  3. L Bretzner et al. Hand gesture recognition using multi-scale colour features, hierarchical models, and particle filtering. Proc IEEE Int Conf on Automatic Face and Gesture Recognition, 2002.

    Google Scholar 

  4. P J Burt and E H Adelson. The Laplacian pyramid as a compact image code. IEEE Trans Comm, pp 532–540, 1983.

    Google Scholar 

  5. R Cutler and M Turk. View-based interpretation of real-time optical flow for gesture recognition. Proc IEEE Int Conf on Automatic Face and Gesture Recognition, 1998.

    Google Scholar 

  6. S Grange et al. Vision-based sensor fusion for human-computer interaction. Proc Int Conf on Intelligent Robots and Systems, 2002.

    Google Scholar 

  7. M Isard and A Blake. Condensation — Conditional density propagation for visual tracking. Int J Computer Vision, 1998.

    Google Scholar 

  8. M Isard and A Blake. A mixed-state CONDENSATION tracker with automatic model-switching. Proc ICCV, 1998.

    Google Scholar 

  9. N Jojić et al. Tracking self-occluding articulated object in dense disparity maps. Proc ICCV, 1999.

    Google Scholar 

  10. R E Kalman. A new approach to linear filtering and prediction problems. ASME J of Basic Engineering, pp 34–45, 1960.

    Google Scholar 

  11. J Kennedy and R Eberhart. Particle swarm optimization. Proc IEEE Int Conf on Neural Networks, 1995.

    Google Scholar 

  12. M Kölsch. Vision based hand gesture interfaces for wearable computing and virtual environments. PhD Thesis, University of California, Santa Barbara, 2004.

    Google Scholar 

  13. M Kölsch and M Turk. Fast 2D hand tracking with flocks of features and multi-cue integration. Proc IEEE Workshop on Real-Time Vision for Human-Computer Interaction, 2004.

    Google Scholar 

  14. M Kölsch and M Turk. Robust hand detection. Proc IEEE Intl Conf on Automatic Face and Gesture Recognition, 2004.

    Google Scholar 

  15. M Kölsch et al. Vision-Based interfaces for mobility. Proc Int Conf on Mobile and Ubiquitous Systems, 2004.

    Google Scholar 

  16. T Kurata et al. The Hand Mouse: GMM hand-color classification and mean-shift tracking. Proc Int Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, 2001.

    Google Scholar 

  17. B D Lucas and T Kanade. An iterative image registration technique with an application to stereo vision. Proc Imaging Understanding Workshop, 1981.

    Google Scholar 

  18. F Quek. Unencumbered gestural interaction. IEEE Multimedia, pp 36–47, 1996.

    Google Scholar 

  19. C W Reynolds. Flocks, herds, and schools: A distributed behavioral model. ACM Trans on Graphics, pp 25–34, 1987.

    Google Scholar 

  20. J Segen and S Kumar. GestureVR: Vision-based 3D hand interface for spatial interaction. Proc ACM Int Multimedia Conference, 1998.

    Google Scholar 

  21. C Shan et al. Real-time hand tracking by combining particle filtering and meanshift. Proc IEEE Int Conf on Automatic Face and Gesture Recognition, 2004.

    Google Scholar 

  22. J Shi and C Tomasi. Good features to track. Proc CVPR, 1994.

    Google Scholar 

  23. B Stenger et al. Filtering using a tree-based estimator. Proc ICCV, 2003.

    Google Scholar 

  24. C R Wren and A P Pentland. Dynamic models of human motion. Proc IEEE Int Conf on Automatic Face and Gesture Recognition, 1998.

    Google Scholar 

  25. Y Wu and T S Huang. Hand modeling, analysis, and recognition. IEEE Signal Proc Mag, pp 51–60, 2001.

    Google Scholar 

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Kölsch, M., Turk, M. (2005). Flocks of Features for Tracking Articulated Objects. In: Kisačanin, B., Pavlović, V., Huang, T.S. (eds) Real-Time Vision for Human-Computer Interaction. Springer, Boston, MA. https://doi.org/10.1007/0-387-27890-7_5

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  • DOI: https://doi.org/10.1007/0-387-27890-7_5

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-27697-7

  • Online ISBN: 978-0-387-27890-2

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