Feature-based sequence-to-sequence matching

International Journal of Computer Vision

Volumn 68, Issue 1, 2006, Pages 53-64

  Caspi, Yaron ^a   Simakov, Denis ^a   Irani, Michal ^a  

^a WEIZMANN INSTITUTE OF SCIENCE   (Israel)

Author keywords

Alignment in space and time; Dynamic information; Multi sensor alignment; Sequence to sequence matching; Trajectory matching; Wide base line matching

Indexed keywords

COMPUTER SIMULATION; COMPUTER VISION; IMAGE RECONSTRUCTION; SENSORS; SYNCHRONIZATION; VIDEO SIGNAL PROCESSING;

ALIGNMENT IN SPACE AND TIME; DYNAMIC INFORMATION'S; SEQUENCE-TO-SEQUENCE MATCHING; TRAJECTORY MATCHING; WIDE BASE-LINE MATCHING;

VIDEO CAMERAS;

EID: 33646576498     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-005-4842-z     Document Type: Conference Paper

References (31)

1. Bergen, J., Anandan, P., Hanna, K., and Hingorani, R. 1992. Hierarchical model-based motion estimation. In European Conference on Computer Vision (ECCV). Santa Margarita Ligure, pp. 237-252.
2. Burt, P. and Kolczynski, R. 1993. Enhanced image capture through fusion. In: International Conference on Computer Vision (ICCV). Berlin, pp. 173-182.
3. Caspi, Y. and Irani, M. 2000. A step towards sequence-to-sequence alignment. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Hilton Head Island, South Carolina, pp. 682-689.
4. Caspi, Y., Simakov, D., and Irani, M. 2002. Feature-based sequence-to-sequence matching. In: ECCV, VAMODS Workshop. Copenhagen.
5. Faugeras, O., Luong, Q., and Papadopoulo, T. 2001. The Geometry of Multiple Images. MIT Press.
6. Ferrari, V.,Tuytelaars, T., and Gool, L.V. 2003. Wide-baseline multiple-view Correspondences. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 1. Madison, Wisconsin, pp. 718-725.
7. Fischler, M.A. and Bolles, R. 1981. RANSAC random sample concensus: A paradigm for model fitting with applications to image analysis and automated cartography. In Communications of the ACM, Vol. 24, pp. 381-395.
8. Giese, M.A. and Poggio, T. 2000. Morphable models for the analysis and synthesis of complex motion patterns. International Journal of Computer Vision 38(1):59-73.
9. Hampel, F., Rousseeuw, P., Ronchetti, F., and Stahel, W. 1986. Robust Statistics: The Approach Based on Influence Functions. John Wiley: New York.
10. Harris, C. and Stephens, M. 1988. A combined corner and edge detector. In 4th Alvey Vision Conference, pp. 147-151.
11. Hartley, R. and Zisserman, A. 2000. Multiple View Geometry in Computer Vision. Cambridge University Press: Cambridge.
12. Kadir, T., Zisserman, A., and Brady, M. 2004. An affine invariant salient region detector. In European Conference on Computer Vision (ECCV). Prague, Czech Republic.
13. Lee, L., Romano, R., and Stein, G. 2000. Monitoring activities from multiple video streams: establishing a common coordinate frame. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI) (Special Issue on Video Surveillance and Monitoring), 22:758-767.
14. Lowe, D.G. 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91-110.
15. Lucas, B. and Kanade, T. 1981, 'An iterative image registration technique with an application to stereo vision. In: Image Understanding Workshop, pp. 121-130.
16. Matas, J., Chum, O., Martin, U., and Pajdla, T. 2002. Robust wide baseline stereo from maximally stable extremal regions. In P.L. Rosin and D. Marshall (eds.), British Machine Vision Conference, Vol. 1. London, UK, pp. 384-393.
17. Mikolajczyk, K. and Schmid, C., 2004. Scale and affine invariant interest point detectors. International Journal of Computer Vision 60(1):63-86.
18. Mindru, F., Tuytelaars, T., Gool, L.V., and Moons, T. 2004. Moment invariants tor recognition under changing viewpoint and illumination. Comput. Vis. Imag Underst., 94(1-3):3-27.
19. Mundy, J. and Zisserman, A. 1992. Geometric invariance in computer vision. In MIT Press.
20. Rousseeuw, P. 1987. Robust Regression and Outlier Detection. Wiley: New York.
21. Shechtman, E.,Caspi, Y., and Irani, M. 2002. Increasing video resolution in tim and space. In European Conference on Computer Vision (ECCV), Copenhagen.
22. Stauffer, C., and Tieu, K., 2003. Automated multi-camera planar tracking correspondence modeling. In IEEE Conference on Computer Vision and Pattern Recognition SCVPRj, Madison, Wisconsin, pp. 259-266.
23. Stein, G.P. 1998. Tracking from multiple view points: self-calibration of space and time. In DARPA III Workshop, Montery CA, pp. 1037-1042.
24. Szeliski, R. and Shum., H.-Y. 1997. Creating full view panoramic image mosaics and environment maps. In Computer Graphics Proceedings, Annual Conference Series, pp. 251-258.
25. Tomasi, C. and Kanade, T. 1991. Detection and tracking of point features. Technical Report CMU-CS-91-132, Carnegie Mellon University.
26. Tresadern, P. and Reid, I. 2003. Synchronizing image sequences of non-rigid objects. In British Machine Vision Conference, Vol. 2, Norwich, pp. 629-638.
27. Tuytelaars, T. and Gool, L.V. 2004. Matching widely separated views based on affine invariant regions. International Journal of Computer Vision, 59(1):61-85.
28. Wong, K.-Y. K. and Cipolla, R. 2001. Structure and motion from silhouettes. In International Conference on Computer Vision (ICCV), Vol. II. Vancouver, Canada, pp. 217-222.
29. Xu, C. and Zhang, Z. 1996. Epipolar Geometry in Stereo, Motion and Object Recognition. Kluwer Academic Publishers: Dordecht, The Netherlands.
30. Zhang, Z., Deriche R., Faugeras, O., and Luong, Q. 1995, 'A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artificial Intelligence 78:87-119.
31. Zoghlami, I.,Faugeras, O., and Deriche, R. 1997. Using geometric corners to build a 2d mosaic from a set of images. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 420-425.