Generating spatiotemporal models from examples

Image and Vision Computing

Volumn 14, Issue 8 SPEC. ISS., 1996, Pages 525-532

  Baumberg, Adam ^a   Hogg, David ^a  

^a UNIVERSITY OF LEEDS   (United Kingdom)

Author keywords

Learning; Modal analysis; Non rigid motion analysis; Spatiotemporal modelling

Indexed keywords

ELASTICITY; LEARNING ALGORITHMS; MATHEMATICAL MODELS; MODELS; TRACKING (POSITION);

CURVE TRACKING; MODAL ANALYSIS; NON-RIGID MOTION ANALYSIS; SPATIOTEMPORAL MODELLING;

COMPUTER VISION;

EID: 0030212464     PISSN: 02628856     EISSN: None     Source Type: Journal    
DOI: 10.1016/0262-8856(96)01092-X     Document Type: Article

References (19)

1. A. Pentland and B. Horowitz, Recovery of non-rigid motion and structure, IEEE Trans. Pattern Analysis and Machine Intelligence, 13(7) (July 1991) 730-742.
2. C. Nastar and N. Ayache, Spatio-temporal analysis of nonrigid motion from 4D data, IEEE Workshop on Motion of Non-rigid and Articulated Objects, pp. 146-151, IEEE Press, November 1994.
3. C. Nastar, Vibration modes for nonrigid analysis in 3D images, Euro. Conf. on Computer Vision, Vol 1, May 1994, pp. 231-238.
4. A. Blake and M. Isard, 3D position, attitude and shape input using video tracking of hands and lips, Proc. ACM Siggraph, 1994, pp. 185-192.
5. A. Blake, M. Isard and D. Reynard, Learning to track the visual motion of contours, Artificial Intelligence, 78 (1995) 101-134.
6. T.J. Cootes, C.J. Taylor, D.H. Cooper and J. Graham, Training models of shape from sets of examples, British Machine Vision Conference, September 1992, pp. 9-18.
7. A. Baumberg and D. Hogg, An efficient method for contour tracking using active shape models, in I.C.S. Press (ed.), IEEE Workshop on Motion of Non-rigid and Articulated Objects, November 1994, pp. 194-199.
8. C. Kervann and F. Heitz, Robust tracking of stochastic deformable models in long image sequences, IEEE International Conference on Image Processing, Vol 3, IEEE Press, November 1994, pp. 88-92.
9. A. Pentland and S. Sclaroff, Closed-form solutions for physically based shape modeling and recognition, IEEE Trans. Pattern Analysis and Machine Intelligence, 13(7) (July 1991) 715-729.
10. R. Gonzalez and R. Woods, Digital Image Processing, Addison-Wesley, 1992.
11. D. Terzopoulos, A. Witkin and M. Kass, Symmetry-seeking models for 3-D object reconstruction, Int. J. Computer Vision, 1(3) (1987) 211-221.
12. A. Baumberg and D. Hogg, An efficient method for contour tracking using active shape models, Technical Report 94.11, School of Computer Studies, University of Leeds, 1994. (http://agoa.leeds.-ac.uk/scs/doc/reports/)
13. A. Baumberg and D. Hogg, Learning flexible models from image sequences, Euro. Conf. Computer Vision, Vol 1, pp. 299-308, May 1994.
14. A. Baumberg and D. Hogg, An adaptive eigenshape model, in D. Pycock (ed.), British Machine Vision Conferrence, Vol 1, BMVA, September 1995, pp. 87-96.
15. A. Blake, R. Curwen and A. Zisserman, A framework for spatiotemporal control in the tracking of visual contours, Int. J. Computer Vision (1993).
16. N.K. Sinha and B. Kuszta, Modeling and Identification of Dynamic Systems, Van Nostrand Reinhold, 1983.
17. A. Baumberg and D. Hogg, Generating spatiotemporal models from training examples. Tech. Rep. 95.9, University of Leeds, 1995. (http://agora.leeds.ac.uk/scs/doc/reports)
18. P. Ciarlet, Introduction to Numerical Linear Algebra and Optimisation, Cambridge University Press, 1989.
19. S. Niyogi and E. Adelson, Analyzing gait with spatiotemporal surfaces, IEEE Workshop on Motion of Non-rigid and Articulated Objects, IEEE Press, November 1994, pp. 64-69.