Rejecting outliers based on correspondence manifold

Zidonghua Xuebao/ Acta Automatica Sinica

Volumn 35, Issue 1, 2009, Pages 17-22

  Li, Xiang Ru ^a   Li, Xiao Ming ^b   Li, Hai Ling ^c   Cao, Mao Yong ^a  

^a SHANDONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b SHANXI UNIVERSITY   (China)

^c Shandong College of Electronic Technology   (China)

Author keywords

Computer vision; Diagnostic; Outlier rejection; Point correspondence

Indexed keywords

IMAGE PROCESSING;

DIAGNOSTIC; ESTIMATED MODELS; EXCELLENT PERFORMANCE; FUNDAMENTAL PROBLEMS; MODEL ESTIMATIONS; MODEL SELECTIONS; OUTLIER REJECTION; PARAMETRIC MODELS; POINT CORRESPONDENCE; REAL IMAGES;

COMPUTER VISION;

EID: 60149106573     PISSN: 02544156     EISSN: None     Source Type: Journal    
DOI: 10.3724/SP.J.1004.2009.00017     Document Type: Article

References (22)

1. Hartley R, Zisserman A. Multiple View Geometry in Computer Vision (Second Edition). Cambridge: Cambridge University Press, 2003.
2. Brown M Z, Burschka D, Hager G D. Advances in computational stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(8): 993-1008.
3. Tico M, Rusu C, Kuosmanen P. A geometric invariant representation for the identification of corresponding points. In: Proceedings of International Conference on Image Processing. Kobe, Japan: IEEE, 1999. 462-466.
4. Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2): 91-110.
5. Hartley R, Schaffalitzky F. L1 minimization in geometric reconstruction problems. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE, 2004. 504-509.
6. ∞-norm. IEEE Transactions on Pattern Recognition and Machine Intelligence, 2008, 30(9): 1603-1617.
7. Torr P H S, Murray D W. Outlier detection and motion segmentation. In: Proceedings of SPIE. Boston, USA: SPIE, 1993. 432-443.
8. Sim K, Hartley R. Removing outliers using the L1 norm. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE, 2006. 485-494.
9. Li H D. A practical algorithm for L1 triangulation with outliers. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE, 2007. 1-8.
10. Rahmatullah I A. Identifying multiple influential observations in linear regression. Journal of Applied Statistics, 2005, 32(9): 929-946.
11. Barnett V, Lewis T. Outliers in Statistical Data (Third Edition). New York: Wiley, 1994.
12. Liang Y Z, Kvalheim O M. Robust methods for multivariate Analysis-a tutorial review. Chemometrics and Intelligent Laboratory Systems, 1996, 32(1): 1-10.
13. Fischler M A, Bolles R C. Random sample consensus: a paradigm for model fitting with application to image analysis and automated cartography. Communications of the Association and Computing Machine, 1981, 24(6): 381-395.
14. Rousseeuw P J. Least median of squares regression. Journal of the American Statistical Association, 1984, 79(388): 871-880.
15. Zhang Z, Deriche R, Faugeras O, Luong Q T. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artificial Intelligent, 1995, 78(1): 87-119.
16. Torr P H S, Zisserman A. MLESAC: a new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding, 2000, 78(1): 138-156.
17. Tordoff B J, Murray D W. Guided-MLESAC: faster image transform estimation by using matching priors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(10): 1523-1535.
18. Sonka M, Hlavac V, Boyle R. Image Processing, Analysis, and Machine Vision (Second Edition). California: Brooks/Cole Publishing, 1999.
19. Triggs B. Matching constraints and the joint image. In: Proceeding of the 5th International Conference on Computer Vision. Washington D. C., USA: IEEE, 1995. 338-343.
20. Chatterjee S, Hadi A S. Influential observations, high leverage points, and outliers in linear regression. Statistical Science, 1986, 1(3): 379-393.
21. Smola A J, Schölkopf B. A tutorial on support vector regression. Statistics and Computing, 2004, 14(3): 199-222.
22. Chang C C, Lin C J. LIBSVM: a library for support vector machines[Online], available: http://www.csie.ntu.edu.tw/-cjlin/libsvm/, 2001.