Tracking in 3D: Image variability decomposition for recovering object pose and illumination

Pattern Analysis and Applications

Volumn 2, Issue 1, 1999, Pages 82-91

  Belhumeur, Peter N ^a   Hager, Gregory D ^a,b,c  

^a Yale University   (United States)

^b IEEE

^c Robotics Automat Soc Tech C

Author keywords

Illumination modelling; Image sequence analysis; Pose estimation; Tracking

Indexed keywords

EID: 0033443934     PISSN: 14337541     EISSN: None     Source Type: Journal    
DOI: 10.1007/s100440050017     Document Type: Article

References (32)

1. Ullman S, Basri R. Recognition by linear combinations of models. IEEE Trans Pattern Analysis and Machine Intelligence 1991;13(10):992-1006
2. Jacobs DW. Space efficient 3D model indexing. Proceedings IEEE Conference Computer Vision Pattern Recognition 1992; 439-444
3. Tomasi C. Kanade T. Shape and motion from image streams under orthography: A factorization method. International Journal of Computer Vision 1992;9(2):137-154
4. Hager GD, Belhumeur PN. Efficient region tracking of with parametric models of illumination and geometry. IEEE Transactions Pattern Analysis Machine Intelligence 1998;20(10):1025-1039
5. Black MJ, Jepson AD. Eigentracking: Robust matching and tracking of articulated objects using a view-based representation. International Journal of Computer Vision 1998;26(1):63-84
6. Shi J, Tomasi C. Good features to track. Proc IEEE Computer Society Conference Computer Vision and Pattern Recognition 1994;593-600
7. Belhumeur PN, Kriegman DJ. What is the set of images of an object under all possible illumination conditions. International Journal of Computer Vision 1998;28(3)
8. Gennery DB. Visual tracking of known three-dimensional objects. International Journal of Computer Vision 1992;7(3):243-270
9. Lowe DG. Robust model-based motion tracking through the integration of search and estimation. International Journal of Computer Vision 1992;8(2):113-122
10. Gavrila DM, Davis LS. Tracking humans in action: A 3D model-based approach. Proc DARPA Image Understanding Workshop 1996, pp 737-746
11. Isard M, Blake A. Contour tracking by stochastic propagation of conditional density. Proceedings European Conference on Computer Vision 1996, pp 343-356
12. Blake A, Isard M. Active Contours. Springer-Verlag, 1998
13. Reynard D, Wildenberg A, Blake A, Marchant J. Learning dynamics of complex motions from image sequences. Proceedings European Conference on Computer Vision 1996, pp 357-368
14. Deriche R, Faugeras O. Tracking line segments. In: O Faugeras (ed), Proceedings European Conference on Computer Vision, Springer-Verlag, 1990, pp 259-268
15. Papanikolopoulos NP, Khosla PK. Adaptive robot visual tracking: theory and experiments. IEEE Trans Automatic Control 1993;38(3):429-445
16. Black MJ, Yacoob Y. Tracking and recognizing rigid and non-rigid facial motions using local parametric models of image motion. International Journal of Computer Vision 1997;25(1):23-48
17. Rehg JM, Witkin AP. Visual tracking with deformation models. Proc IEEE International Conference Robot Automat 1991, pp 844-850
18. Murase H, Nayar S. Visual learning and recognition of 3-D objects from appearance. International Journal of Computer Vision 1995;14(5-24)
19. Pentland A, Moghaddam B, Starner. View-based and modular eigenspaces for face recognition. Proceedings IEEE Conference Computer Vision Pattern Recognition 1994, pp 84-91
20. Poggio T, Sung KK. Example-based learning for view-based human face detection. Proc Image Understanding Workshop 1994;II:843-850
21. Sirovitch L, Kirby M. Low-dimensional procedure for the characterization of human faces. Journal of Optical Society America A 1987;2:519-524
22. Turk M, Pentland A. Eigenfaces for recognition. Journal of Cognitive Neuroscience 1991;3(1)
23. Goresky M, Macpherson R. Stratified Morse Theory. Springer-Verlag, 1980
24. Lucas BD, Kanade T. An iterative image registration technique with an application to stereo vision. Proceedings International Joint Conference on Artificial Intelligence 1981, pp 674-679
25. Koenderink JJ, Van Doorn AJ. Affine structure from motion. Journal of Optical Society Am 1991;8(2):337-385
26. Belhumeur PN. A Bayesian approach to binocular stereopsis. International Journal of Computer Vision 1996;19
27. Hallinan P. A Deformable Model for Face Recognition Under Arbitrary Lighting Conditions. PhD thesis, Harvard University, 1995
28. Moses Y, Adini Y, Ullman S. Face recognition: The problem of compensating for changes in illumination direction. European Conference Computer Vision 1994, pp 286-296
29. Shashua A. Geometry and Photometry in 3D Visual Recognition. PhD thesis, MIT, 1992
30. Paul R. Robot Manipulators. MIT Press, 1981
31. Hager GD, Toyama K. The "XVision" system: A general purpose substrate for real-time vision applications. Computer Vision, Image Understanding 1998;69(1):23-27
32. Stein GP, Shashua A. Direct estimation of motion and extended scene structure from a moving stereo rig. Proceedings IEEE Conference Computer Vision Pattern Recognition 1998, pp 211-218