A model-selection framework for multibody structure-and-motion of image sequences

International Journal of Computer Vision

Volumn 79, Issue 2, 2008, Pages 159-177

  Schindler, Konrad ^a   Suter, David ^b   Wang, Hanzi ^c  

^a ETH ZURICH   (Switzerland)

^b MONASH UNIVERSITY   (Australia)

^c Johns Hopkins University   (United States)

Author keywords

3D motion segmentation; Model selection; Multibody structure and motion

Indexed keywords

FEATURE EXTRACTION; IMAGE SEGMENTATION; MATHEMATICAL MODELS; MOTION ESTIMATION; MOTION PICTURES;

MODEL SELECTION; MOTION SEGMENTATION; MULTIBODY STRUCTURE AND MOTION;

IMAGE ANALYSIS;

EID: 43949145309     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-007-0111-7     Document Type: Article

References (42)

1. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. Petrov, F. Csaki (Eds.), Proceedings of the 2nd international symposium of information theory (pp. 267-281).
2. Bozdogan, H. (1987). Model selection and Akaike's information criterion (AIC): the general theory and its analytical extensions. Psychometrika, 52(3), 345-370.
3. Chum, O., Werner, T., & Matas, J. (2005). Two-view geometry estimation unaffected by a dominant plane. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 772-779), San Diego, CA.
4. Costeira, J., & Kanade, T. (1995). A multi-body factorization method for motion analysis. In Proceedings of the 5th international conference on computer vision (pp. 1071-1077), Boston, MA.
5. Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification (2nd ed.). New York: Wiley.
6. Faugeras, O., Luong, Q.-T., & Papadopoulo, T. (2001). The geometry of multiple images. Cambridge: MIT Press.
7. Fitzgibbon, A. W., & Zisserman, A. (2000). Multibody structure and motion: 3-d reconstruction of independently moving objects. In Proceedings of the 6th European conference on computer vision (pp. 891-905), Dublin, Ireland.
8. Frahm, J.-M., & Pollefeys, M. (2006). RANSAC for (quasi-) degenerate data (QDEGSAC). In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 453-460), New York.
9. Han, M., & Kanade, T. (2000). Reconstruction of scenes with multiple linearly moving objects. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 542-549), Hilton Head, South Carolina.
10. Hartley, R., & Zisserman, A. (2000). Multiple view geometry in computer vision. Cambridge: Cambridge University Press.
11. Irani, M., & Anandan, P. (1998). A unified approach to moving object detection in 2D and 3D scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(6), 577-589.
12. Kanatani, K. (2004). Uncertainty modeling and model selection for geometric inference. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(10), 1307-1319.
13. Kverh, B., & Leonardis, A. (2004). A generalisation of model selection criteria. Pattern Analysis and Applications, 7(1), 51-65.
14. Langs, G., Peloschek, P., & Bischof, H. (2005). Optimal sub-shape models by minimum description length. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 310-315), San Diego, CA.
15. Leonardis, A., Gupta, A., & Bajcsy, R. (1995). Segmentation of range images as the search for geometric parametric models. International Journal of Computer Vision, 14(1), 253-277.
16. Leontaritis, I. J., & Billings, S. A. (1987). Model selection and validation methods for non-linear systems. International Journal of Control, 45(1), 311-341.
17. Li, T., Khallem, V., Singaraju, D., & Vidal, R. (2007). Projective factorization of multiple rigid-body motions. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 1-6), Minneapolis, MI.
18. Ma, Y., Kosecka, J., Soatto, S., & Sastry, S. (2003). An invitation to 3-D vision. Berlin: Springer.
19. Matsunaga, C., & Kanatani, K. (2000). Calibration of a moving camera using a planar pattern: optimal computation, reliability evaluation and stabilization by model selection. In Proceedings of the 6th European conference on computer vision (Vol. 2, pp. 595-609), Dublin, Ireland.
20. Maybank, S. J., & Sturm, P. F. (1999). MDL, collineations and the fundamental matrix. In Proceedings of the 10th British machine vision conference, Nottingham, UK.
21. Nemhauser, G., Wolsey, L., & Fisher, M. (1978). An analysis of approximations for maximizing submodular set functions I. Mathematical Programing, 14, 265-294.
22. Nistér, D. (2004). An efficient solution to the five-point relative pose problem. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(6), 756-770.
23. Ripley, B. D. (1996). Pattern recognition and neural networks. Cambridge: Cambridge University Press.
24. Rissanen, J. (1978). Modeling by shortest data description. Automatica, 14, 465-471.
25. Rissanen, J. (1984). Universal coding, information, prediction and estimation. IEEE Transactions on Information Theory, 30, 629-636.
26. Schindler, K., & Suter, D. (2005). Two-view multibody structure-and-motion with outliers. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 676-683), San Diego, CA.
27. Schindler, K., U, J., & Wang, H. (2006). Perspective n-view multibody structure-and-motion through model selection. In Proceedings of the 9th European conference on computer vision (pp. 606-619), Graz, Austria.
28. Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 497-511.
29. Shannon, C. E. (1948). A mathematical theory of communication. Bell Systems Technical Journal, 27, 379-423.
30. Stricker, M., & Leonardis, A. (1995). ExSel++: a general framework to extract parametric models. In Proceedings of computer analysis of images and patterns (pp. 90-97).
31. Tomasi, C., & Kanade, T. (1991). Detection and tracking of point features. Technical report CMU-CS-91-132, Carnegie Mellon University.
32. Tong, W.-S., Tang, C.-K., & Medioni, G. (2004). Simultaneous two-view epipolar geometry estimation and motion segmentation by 4D tensor voting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(9), 1167-1184.
33. Torr, P. H. S. (1998). Geometric motion segmentation and model selection. Philosophical Transactions of the Royal Society of London A, 356(1740), 1321-1340.
34. Torr, P. H. S. (2000). Model selection for structure and motion recovery from multiple images. In A. Bab-Hadiashar & D. Suter (Eds.), Data segmentation and model selection for computer vision. Berlin: Springer.
35. Vidal, R., & Hartley, R. (2004). Motion segmentation with missing data using PowerFactorization and GPCA. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 310-316), Washington, DC.
36. Vidal, R., & Ma, Y. (2004). A unified algebraic approach to 2-D and 3-D motion segmentation. In Proceedings of the 8th European conference on computer vision (pp. 1-15), Prague, Czech Republic.
37. Vidal, R., Soatto, S., Ma, Y., & Sastry, S. (2002). Segmentation of dynamic scenes from the multibody fundamental matrix. In Proceedings ECCV workshop on visual modeling of dynamic scenes.
38. Wallace, C. S., & Boulton, D. M. (1968). An information measure for classification. Computer Journal, 11(2), 185-194.
39. Wallace, C. S., & Freeman, P. R. (1987). Estimation and inference by compact coding. Journal of the Royal Statistical Society Series B, 49(3), 240-265.
40. Wang, H., & Suter, D. (2004). Robust fitting by adaptive-scale residual consensus. In Proceedings of the 8th European conference on computer vision (pp. 107-118), Prague, Czech Republic.
41. Wills, J., Agarwal, S., & Belongie, S. (2003). What went where. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 37-44), Madison, WI.
42. Wolf, L., & Shashua, A. (2001). Two-body segmentation from two perspective views. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 263-270), Kauai, Hawaii.