Detecting incorrect feature tracking by affine space fitting

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

Volumn 5414 LNCS, Issue , 2009, Pages 191-202

  Takada, Chika ^a   Sugaya, Yasuyuki ^a  

^a TOYOHASHI UNIVERSITY OF TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

AFFINE SPACES; CAMERA MODELS; FEATURE POINT TRACKING; FEATURE-TRACKING; IMAGE NOISE; PARAMETER SPACES; PARTIAL TRAJECTORIES; STATISTICAL MODELS; VIDEO IMAGES;

OPTICAL FLOWS;

EID: 60149102991     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-92957-4_17     Document Type: Conference Paper

References (19)

1. Brandt, S.: Closed-form solutions for affine reconstruction under missing data. In: Proc. Statistical Methods in Video Processing Workshop, Copenhagen, Denmark, June 2002, pp. 109-114 (2002)
2. Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. ACM 24(6), 381-395 (1981)
3. Hartley, R., Horseman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2000)
4. Huynh, D.Q., Heyden, A.: Outlier detection in video sequences under affine projection. In: Proc. IEEE Conf. Comput. Vision Pattern Recog., Kauai, HI, U.S.A, December 2001, vol. 2, pp. 695-701 (2001)
5. Ichimura, N.: Stochastic filtering for motion trajectory in image sequences using a Monte Carlo filter with estimation of hyper-parameters. In: Proc. 16th Int. Conf. Pattern Recog., Quebec City, Canada, August 2002, vol. 4, pp. 68-73 (2002)
6. Ichimura, N., Ikoma, N.: Filtering and smoothing for motion trajectory of feature point using non-gaussian state space model. IEICE Trans. Inf. Syst. E84-D(6), 755-759 (2001)
7. Jacobs, D.W.: Linear fitting with missing data for structure-from-motion. Comput. Vision Image Understand. 82(1), 57-81 (2001)
8. Kanatani, K.: Statistical Optimization for Geometric Computation: Theory and Practice. Elsevier Scence, Amsterdam (1996)
9. Kanatani, K.: Motion segmentation by subspace separation and model selection. In: Proc. 8th Int. Conf. Comput. Vision, Vancouver, Canada, vol. 2, pp. 301-306 (2001)
10. Kanatani, K.: Motion segmentation by subspace separation: Model selection and reliability evaluation. Int. J. Image Graphics 2(2), 179-197 (2002)
11. Kanatani, K.: Evaluation and selection of models for motion segmentation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 335-349. Springer, Heidelberg (2002)
12. Poelman, C.J., Kanade, T.: A paraperspective factorization method for shape and motion recovery. IEEE Trans. Patt. Anal. Mach. Intell. 19(3), 206-218 (1997)
13. Saito, H., Kamijima, S.: Factorization method using interpolated feature tracking via projective geometry. In: Proc. 14th British Machine Vision Conf., Norwich, UK, vol. 2, pp. 449-458 (September 2003)
14. Sugaya, Y., Kanatani, K.: Automatic camera model selection for multibody motion segmentation. In: Proceedings of the IAPR Workshop on Machine Vision Applications (MVA 2002), Nara, Japan, 11-13 December, pp. 412-415 (2002)
15. Sugaya, Y., Kanatani, K.: Outlier removal for motion tracking by subspace separation. IEICE Trans. Inf. & Syst. E86-D(6), 1095-1102 (2003)
16. Sugaya, Y., Kanatani, K.: Extending interrupted feature point tracking for 3-D affine reconstruction. IEICE Transactions on Information and Systems E87-D(4), 1031-1038 (2004)
17. Sugaya, Y., Kanatani, K.: Multi-stage optimization for multi-body motion segmentation. IEICE Transactions on Information and Systems E87-D(7), 1935-1942 (2004)
18. Tomasi, C., Kanade, T.: Shape and motion from image streams under orthography-A factorization method. Int. J. Comput. Vision 9(2), 137-154 (1992)
19. Tomasi, C., Kanade, T.: Detection and Tracking of Point Features, CMU Tech. Rep. CMU-CS-91-132 (April 1991), http://www.ces.clemson.edu/~stb/klt/