Incremental discovery of object parts in video sequences

Computer Vision and Image Understanding

Volumn 110, Issue 1, 2008, Pages 60-74

  Drouin, Stéphane ^a   Hébert, Patrick ^a   Parizeau, Marc ^a  

^a UNIVERSITÉ LAVAL   (Canada)

Author keywords

Incremental modeling; Motion based segmentation; Time scale selection; Tracking; Vision and scene understanding

Indexed keywords

COMPUTER SIMULATION; COMPUTER VISION; IMAGE ANALYSIS; IMAGE RECOGNITION; MATHEMATICAL MODELS; PROBLEM SOLVING;

INCREMENTAL MODELING; MOTION BASED SEGMENTATION; TIME SCALE SELECTION; VISION AND SCENE UNDERSTANDING;

IMAGE SEGMENTATION;

EID: 40649126332     PISSN: 10773142     EISSN: 1090235X     Source Type: Journal    
DOI: 10.1016/j.cviu.2007.04.001     Document Type: Article

References (28)

1. Gavrila D.M. The visual analysis of human movement: a survey. Computer Vision and Image Understanding 73 1 (1999) 82-98
2. J. Deutscher, A. Blake, I. Reid, Articulated body motion capture by annealed particle filtering, in: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, 2000, pp. 126-133.
3. Nickels K., and Hutchinson S. Model-based tracking of complex articulated objects. IEEE Transactions on Robotics and Automation 17 1 (2001) 28-36
4. H. Sidenbladh, M.J. Black, D.J. Fleet, Stochastic tracking of 3D human figures using 2D image motion, in: European Conference on Computer Vision, vol. 2, 2000, pp. 701-718.
5. C.W. Chu, O.C. Jenkins, M.J. Matarić, Markerless kinematic model and motion capture from volume sequences, in: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, 2003, pp. 475-482.
6. M.P. Kumar, P. Torr, A. Zisserman, Learning layered motion segmentation of video, in: IEEE International Conference on Computer Vision, vol. 1, 2005, pp. 33-40.
7. J. Yan, M. Pollefeys, Automatic kinematic chain building from feature trajectories of articulated objects, in: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, 2006, pp. 712 - 719.
8. J. Yan, M. Pollefeys. A general framework for motion segmentation: Independent, articulated, rigid, non-rigid, degenerate and non-degenerate, in: European Conference on Computer Vision, vol. 4, 2006, pp. 94-106.
9. M.K. Titsias, C.K.I. Williams, Fast unsupervised greedy learning of multiple objects and parts from video, in: IEEE Conference on Computer Vision and Pattern Recognition Workshop, 2004, pp. 179-187.
10. Veeraraghavan H., Schrater P., and Papanikolopoulos N.P. Robust target detection and tracking through integration of motion, color, and geometry. Computer Vision and Image Understanding 103 2 (2006) 121-138
11. Krahnstoever N., Yeasin M., and Sharma R. Automatic acquisition and initialization of articulated models. Machine Vision and Applications 14 4 (2003) 218-228
12. N. Krahnstoever, R. Sharma, Articulated models from video, in: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, 2004, pp. 894-901.
13. A. Levin, Y. Weiss, Learning to combine bottom-up and top-down segmentation, in: European Conference on Computer Vision, vol. 4, 2006, pp. 581-594.
14. S. Drouin, P. Hébert, M. Parizeau, Incremental discovery of object parts in video sequences, in: IEEE International Conference on Computer Vision, vol. 2, 2005, pp. 1754-1761.
15. Matthews I., Ishikawa T., and Baker S. The template update problem. IEEE Transactions on Pattern Analysis and Machine Intelligence 26 6 (2004) 810-815
16. Wang Y., Ostermann J., and Zhang Y.-Q. Video Processing and Communications (2002), Prentice-Hall, Englewood Cliffs, NJ
17. R. Lublinerman, M. Sznaier, O. Camps, Dynamics based robust motion segmentation, in: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, 2006, pp. 1176-1184.
18. Camurri A., Lagerlöf I., and Volpe G. Recognizing emotion from dance movement: comparison of spectator recognition and automated techniques. International Journal of Human-Computer Studies 59 1-2 (2003) 213-225
19. J. Shi, C. Tomasi, Good features to track, in: IEEE Conference on Computer Vision and Pattern Recognition, 1994, pp. 593-600.
20. J.-Y. Bouguet, Pyramidal implementation of the Lucas Kanade feature tracker, Tech. Rep. included into OpenCV distribution, Intel Corporation (2000).
21. J. Wills, S. Agarwal, S. Belongie, What went where, in: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, 2003, pp. 37-44.
22. D. Dubois, H. Prade, Fuzzy sets and probability: misunderstandings, bridges and gaps, in: IEEE Conference on Fuzzy Systems, 1993, pp. 1059-1068.
23. Forsyth D.A., and Ponce J. Computer Vision: A Modern Approach. 1st ed. (2003), Prentice Hall, Englewood Cliffs, NJ
24. Canny J.F. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8 6 (1986) 679-698
25. Rousseeuw P.J. Tutorial to robust statistics. Journal of Chemometrics 5 1 (1991) 1-20
26. Dunn J. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics 3 3 (1973) 32-57
27. Schwarz G. Estimating the dimension of a model. The Annals of Statistics 6 2 (1978) 461-464
28. Boykov Y., Veksler O., and Zabih R. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23 11 (2001) 1222-1239