Hand pose estimation using hierarchical detection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3058, Issue , 2004, Pages 105-116

  Stenger, B ^a,c   Thayananthan, A ^a   Torr, P H S ^b   Cipolla, R ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b OXFORD BROOKES UNIVERSITY   (United Kingdom)

^c TOSHIBA CORPORATION   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER VISION; MOTION ESTIMATION; OBJECT RECOGNITION;

ARTICULATED OBJECT; CASCADE OF CLASSIFIERS; DESIGN CLASSIFIERS; DESIGN OF CLASSIFIERS; HAND POSE ESTIMATIONS; HIERARCHICAL OBJECTS; MULTIPLE PATTERNS; TEMPLATE-BASED CLASSIFIER;

HUMAN COMPUTER INTERACTION;

EID: 27744585336     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-24837-8_11     Document Type: Article

References (23)

1. V. Athitsos and S. Sclaroff. Estimating 3D hand pose from a cluttered image. In Proc. Conf. Computer Vision and Pattern Recognition, volume II, pages 432-439, Madison, USA, June 2003.
2. H. G. Barrow, J. M. Tenenbaum, R. C. Bolles, and H. C. Wolf. Parametric correspondence and chamfer matching: Two new techniques for image matching. In Proc. 5th Int. Joint Conf. Artificial Intelligence, pages 659-663, 1977.
3. F. C. Crow. Summed-area tables for texture mapping. In siggraph, volume 18, pages 207-212, July 1984.
4. nd edition, 2001.
5. P. F. Felzenszwalb. Learning models for object recognition. In Proc. Conf. Computer Vision and Pattern Recognition, volume I, pages 56-62, Kauai, USA, December 2001.
6. D. M. Gavrila. Pedestrian detection from a moving vehicle. In Proc. 6th European Conf. on Computer Vision, volume II, pages 37-49, Dublin, Ireland, June/July 2000.
7. N. J. Gordon, D. J. Salmond, and A. F. M. Smith. Novel approach to non-linear and non-gaussian bayesian state estimation. IEE Proceedings-F, 140:107-113, 1993.
8. D. P. Huttenlocher. Monte carlo comparison of distance transform based matching measure. In ARPA Image Understanding Workshop, pages 1179-1183, New Orleans, USA, May 1997.
9. D. P. Huttenlocher, G. A. Klanderman, and W. J. Rucklidge. Comparing images using the hausdorff distance. IEEE Trans. Pattern Analysis and Machine Intell., 15(9):850-863, 1993.
10. D. P. Huttenlocher, J. J. Noh, and W. J. Rucklidge. Tracking non-rigid objects in complex scenes. In Proc. 4th Int. Conf. on Computer Vision, pages 93-101, Berlin, May 1993.
11. M. Isard and A. Blake. Visual tracking by stochastic propagation of conditional density. In Proc. 4th European Conf. on Computer Vision, pages 343-356, Cambridge, UK, April 1996.
12. I. H. Jermyn and H. Ishikawa. Globally optimal regions and boundaries. In Proc. 7th Int. Conf. on Computer Vision, volume I, pages 20-27, Corfu, Greece, September 1999.
13. J. MacCormick and M. Isard. Partitioned sampling, articulated objects, and interface-quality hand tracking. In Proc. 6th European Conf. on Computer Vision, volume 2, pages 3-19, Dublin, Ireland, June 2000.
14. C. F. Olson and D. P. Huttenlocher. Automatic target recognition by matching oriented edge pixels. Transactions on Image Processing, 6(1):103-113, January 1997.
15. S. Romdhani, P. H. S. Torr, B. Schölkopf, and A. Blake. Computationally efficient face detection. In Proc. 8th Int. Conf. on Computer Vision, volume II, pages 695-700, Vancouver, Canada, July 2001.
16. H. Schneiderman and T. Kanade. A statistical method for 3D object detection applied to faces and cars. In Proc. Conf. Computer Vision and Pattern Recognition, volume I, pages 746-751, Hilton Head Island, USA, June 2000.
17. G. Shakhnarovich, P. Viola, and T. Darrell. Fast pose estimation with parameter-sensitive hashing. In Proc. 9th Int. Conf. on Computer Vision, pages 750-757, Nice, France, October 2003.
18. B. Stenger. Model-Based Hand Tracking Using A Hierarchical Filter. PhD thesis, University of Cambridge, Cambridge, U.K., 2004.
19. B. Stenger, A. Thayananthan, P. H. S. Torr, and R. Cipolla. Filtering using a tree-based estimator. In Proc. 9th Int. Conf. on Computer Vision, volume II, pages 1063-1070, Nice, France, October 2003.
20. A. Thayananthan, B. Stenger, P. H. S. Torr, and R. Cipolla. Learning a kinematic prior for tree-based filtering. In Proc. British Machine Vision Conference, volume 2, pages 589-598, Norwich, UK, September 2003.
21. K. Toyama and A. Blake. Probabilistic tracking with exemplars in a metric space. Int. Journal of Computer Vision, pages 9-19, June 2002.
22. P. Viola and M. J. Jones. Rapid object detection using a boosted cascade of simple features. In Proc. Conf. Computer Vision and Pattern Recognition, volume I, pages 511-518, Kauai, USA, December 2001.
23. O. Williams, A. Blake, and R. Cipolla. A sparse probabilistic learning algorithm for real-time tracking. In Proc. 9th Int. Conf. on Computer Vision, volume I, pages 353-360, Nice, France, October 2003.