Robust point matching via vector field consensus

IEEE Transactions on Image Processing

Volumn 23, Issue 4, 2014, Pages 1706-1721

  Ma, Jiayi ^a   Zhao, Ji ^b   Tian, Jinwen ^a   Yuille, Alan L ^c   Tu, Zhuowen ^d  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b CARNEGIE MELLON UNIVERSITY   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

matching; outlier removal; Point correspondence; regularization

Indexed keywords

ALGORITHMS; BAYESIAN NETWORKS; ESTIMATION; GEOMETRY; STATISTICS;

COMPUTATIONALLY EFFICIENT; EPIPOLAR LINE CONSTRAINTS; MATCHING; MAXIMUM A POSTERIORI ESTIMATION; OUTLIER REMOVALS; POINT CORRESPONDENCE; REGULARIZATION; REPRODUCING KERNEL HILBERT SPACES;

IMAGE MATCHING;

EID: 84896454883     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2014.2307478     Document Type: Article

References (68)

1. M. A. Álvarez and N. D. Lawrence, "Computationally efficient convolved multiple output Gaussian processes," J. Mach. Learn. Res., vol. 12, no. 1, pp. 1425-1466, 2011.
2. N. Aronszajn, "Theory of reproducing kernels," Trans. Amer. Math. Soc., vol. 68, no. 3, pp. 337-404, 1950.
3. L. Baldassarre, L. Rosasco, A. Barla, and A. Verri, "Multi-output learning via spectral filtering," Mach. Learn., vol. 87, no. 3, pp. 259-301, 2012.
4. S. Belongie, J. Malik, and J. Puzicha, "Shape matching and object recognition using shape contexts," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 24, pp. 509-522, Apr. 2002. (Pubitemid 34448769)
5. P. J. Besl and N. D. McKay, "A method for registration of 3-D shapes," IEEE Trans. Pattern Anal. Mach. Intell., vol. 14, no. 2, pp. 239-256, Feb. 1992.
6. C. M. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer-Verlag, 2006.
7. M. J. Black and A. Rangarajan, "On the unification of line processes, outlier rejection and robust statistics with applications in early vision," Int. J. Comput. Vis., vol. 19, no. 1, pp. 57-91, 1996. (Pubitemid 126713881)
8. P. Boyle and M. Frean, "Dependent Gaussian processes," in Advances in Neural Information Processing Systems, Cambridge, MA, USA: MIT Press, 2005, pp. 217-224.
9. B. Cabral and L. C. Leedom, "Imaging vector fields using line integral convolution," in Proc. 20th Annu. Conf. Comput. Graph. Interactive Tech., vol. 27. 1993, pp. 263-270.
10. C. Carmeli, E. De Vito, and A. Toigo, "Vector valued reproducing kernel Hilbert spaces of integrable functions and mercer theorem," Anal. Appl., vol. 4, no. 4, pp. 377-408, 2006.
11. M. Cho and K. M. Lee, "Progressive graph matching: Making a move of graphs via probabilistic voting," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2012, pp. 398-405.
12. H. Chui and A. Rangarajan, "A new point matching algorithm for nonrigid registration," Comput. Vis. Image Understand., vol. 89, nos. 2-3, pp. 114-141, 2003.
13. O. Chum and J. Matas, "Matching with PROSAC-Progressive sample consensus," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2005, pp. 220-226.
14. O. Chum, J. Matas, and J. Kittler, "Locally optimized RANSAC," in Proc. Pattern Recognit. Symp. German Assoc. Pattern Recognit. (DAGM), 2003, pp. 236-243.
15. O. Chum, T. Werner, and J. Matas, "Two-view geometry estimation unaffected by a dominant plane," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2005, pp. 772-779. (Pubitemid 43897272)
16. T. Corpetti, E. Mémin, and P. Pérez, "Dense estimation of fluid flows," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 3, pp. 365-380, Mar. 2002. (Pubitemid 35289352)
17. A. Dempster, N. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. R. Statist. Soc. Series B, vol. 39, no. 1, pp. 1-38, 1977.
18. M. A. Fischler and R. C. Bolles, "Random sample consensus: A paradigm for model fitting with application to image analysis and automated cartography," Commun. ACM, vol. 24, no. 6, pp. 381-395, 1981.
19. D. Geiger and A. L. Yuille, "A common framework for image segmentation," Int. J. Comput. Vis., vol. 6, no. 3, pp. 227-243, 1991.
20. S. Geman and D. Geman, "Stochastic relaxation, gibbs distributions, and the Bayesian restoration of images," IEEE Trans. Pattern Anal. Mach. Intell., vol. 6, no. 6, pp. 721-741, Nov. 1984. (Pubitemid 15453722)
21. S. Gold, A. Rangarajan, C. P. Lu, S. Pappu, and E. Mjolsness, "New algorithms for 2-D and 3-D point matching: Pose estimation and correspondence," Pattern Recognit., vol. 31, no. 8, pp. 1019-1031, 1998. (Pubitemid 128369831)
22. R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, 2nd ed. Cambridge, U.K.: Cambridge Univ. Press, 2003.
23. P. J. Huber, Robust Statistics. New York, NY, USA: Wiley, 1981.
24. A. E. Johnson and M. Hebert, "Using spin-images for efficient object recognition in cluttered 3-D scenes," IEEE Trans. Pattern Anal. Mach. Intell., vol. 21, no. 5, pp. 433-449, May 1999.
25. M. Leordeanu and M. Hebert, "A spectral technique for correspondence problems using pairwise constraints," in Proc. Int. Conf. Comput. Vis., 2005, pp. 1482-1489. (Pubitemid 44042353)
26. X. Li and Z. Hu, "Rejecting mismatches by correspondence function," Int. J. Comput. Vis., vol. 89, no. 1, pp. 1-17, 2010.
27. B. Lin, S. Yang, C. Zhang, J. Ye, and X. He, "Multi-task vector field learning," in Advances in Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 2012, pp. 296-304.
28. C. Liu, J. Yuen, and A. Torralba, "SIFT flow: Dense correspondence across different scenes and its applications," IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 5, pp. 978-994, May 2011.
29. H. Liu and S. Yan, "Common visual pattern discovery via spatially coherent correspondence," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2010, pp. 1609-1616.
30. H. Liu and S. Yan, "Robust graph mode seeking by graph shift," in Proc. Int. Conf. Mach. Learn., 2010, pp. 671-678.
31. D. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, 2004.
32. H. Lu and A. L. Yuille, "Ideal observers for detecting motion: Correspondence noise," in Advances in Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 2006.
33. J. Ma, J. Zhao, J. Tian, X. Bai, and Z. Tu, "Regularized vector field learning with spare approximation for mismatch removal," Pattern Recognit., vol. 46, no. 12, pp. 3519-3532, 2013.
34. J. Ma, J. Zhao, J. Tian, Z. Tu, and A. Yuille, "Robust estimation of nonrigid transformation for point set registration," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2013, pp. 2147-2154.
35. J. Ma, J. Zhao, Y. Zhou, and J. Tian, "Mismatch removal via coherent spatial mapping," in Proc. Int. Conf. Image Process., 2012, pp. 1-4.
36. I. Macêdo and R. Castro, "Learning divergence-free and curl-free vector fields with matrix-valued kernels," Instituto Nacional de Matematica Pura e Aplicada, Brasil, Tech. Rep. , 2008.
37. C. A. Micchelli and M. Pontil, "On learning vector-valued functions," Neural Comput., vol. 17, no. 1, pp. 177-204, 2005. (Pubitemid 40305887)
38. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, et al., "A comparison of affine region detectors," Int. J. Comput. Vis., vol. 65, no. 1, pp. 43-72, 2005. (Pubitemid 43281159)
39. A. Myronenko and X. Song, "Point set registration: Coherent point drift," IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 12, pp. 2262-2275, Dec. 2010.
40. T. Poggio and F. Girosi, "Networks for approximation and learning," Proc. IEEE, vol. 78, no. 9, pp. 1481-1497, Sep. 1990.
41. T. Poggio, V. Torre, and C. Koch, "Computational vision and regularization theory," Nature, vol. 317, no. 6035, pp. 314-319, 1985. (Pubitemid 15232518)
42. R. Raguram, J. M. Frahm, and M. Pollefeys, "A comparative analysis of RANSAC techniques leading to adaptive real-time random sample consensus," in Proc. Eur. Conf. Comput. Vis., 2008, pp. 500-513.
43. C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning. Cambridge, MA, USA: MIT Press, 2006.
44. R. Rifkin, G. Yeo, and T. Poggio, "Regularized least-squares classification," in Advances in Learning Theory: Methods, Model and Applications. Cambridge, MA, USA: MIT Press, 2003.
45. S. Roth and M. J. Black, "On the spatial statistics of optical flow," Int. J. Comput. Vis., vol. 74, no. 1, pp. 33-50, 2007. (Pubitemid 46673102)
46. P. J. Rousseeuw and A. Leroy, Robust Regression and Outlier Detection. New York, NY, USA: Wiley, 1987.
47. M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis, and Machine Vision, 2nd ed. Pacific Grove, CA, USA: Brooks/Cole Company, 1999.
48. A. N. Tikhonov and V. Y. Arsenin, Solutions of Ill-posed Problems. Washington, DC, USA: Winston, 1977.
49. P. H. S. Torr and A. Zisserman, "MLESAC: A new robust estimator with application to estimating image geometry," Comput. Vis. Image Understand., vol. 78, no. 1, pp. 138-156, 2000. (Pubitemid 30596414)
50. L. Torresani, V. Kolmogorov, and C. Rother, "Feature correspondence via graph matching: Models and global optimization," in Proc. Eur. Conf. Comput. Vis., 2008, pp. 596-609.
51. Q.-H. Tran, T.-J. Chin, G. Carneiro, M. S. Brown, and D. Suter, "In defence of RANSAC for outlier rejection in deformable registration," in Proc. Eur. Conf. Comput. Vis., 2012, pp. 274-287.
52. T. Tuytelaars and L. van Gool, "Matching widely separated views based on affine invariant regions," Int. J. Comput. Vis., vol. 59, no. 1, pp. 61-85, 2004.
53. S. Ullman, The Interpretation of Visual Motion, vol. 28, Cambridge, MA, USA: MIT Press, 1979.
54. S. Ullman and A. L. Yuille, Rigidity and Smoothness of Motion. Cambridge, MA, USA: MIT Press, 1987.
55. S. Umeyama, "Least-squares estimation of transformation parameters between two point patterns," IEEE Trans. Pattern Anal. Mach. Intell., vol. 13, no. 4, pp. 376-380, Apr. 1991. (Pubitemid 21645719)
56. A. Vedaldi and B. Fulkerson, "VLFeat-An open and portable library of computer vision algorithms," in Proc. 18th Annu. ACM Int. Conf. Multimedia, 2010, pp. 1469-1472.
57. G. Wahba, Spline Models for Observational Data. Philadelphia, PA, USA: SIAM, 1990.
58. H. Wang, D. Mirota, M. Ishii, and G. D. Hager, "Robust motion estimation and structure recovery from endoscopic image sequences with an adaptive scale kernel consensus estimator," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2008, pp. 1-7.
59. Y. Weiss and E. H. Adelson, "Slow and smooth: A Bayesian theory for the combination of local motion signals in human vision," Massachusetts Inst. Technol., Cambridge, MA, USA, Tech. Rep. 1624, 1998.
60. S. Wu, H. Lu, and A. L. Yuille, "Model selection and parameter estimation in motion perception," in Advances in Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 2008.
61. L. Xu and A. L. Yuille, "Robust principal component analysis by selforganizing rules based on statistical physics approach," IEEE Trans. Neural Netw., vol. 6, no. 1, pp. 131-144, Jan. 1995.
62. S. Yu, V. Tresp, and K. Yu, "Robust multi-task learning with t-processes," in Proc. Int. Conf. Mach. Learn., 2007, pp. 1103-1110.
63. A. L. Yuille, "Generalized deformable models, statistical physics, and matching problems," Neural Comput., vol. 2, no. 1, pp. 1-24, 1990.
64. A. L. Yuille and N. M. Grzywacz, "A computational theory for the perception of coherent visual motion," Nature, vol. 333, no. 6168, pp. 71-74, 1988.
65. A. L. Yuille and N. M. Grzywacz, "A mathematical analysis of the motion coherence theory," Int. J. Comput. Vis., vol. 3, no. 2, pp. 155-175, 1989.
66. A. Zaharescu, E. Boyer, K. Varanasi, and R. Horaud, "Surface feature detection and description with applications to mesh matching," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2009, pp. 373-380.
67. J. Zhao, J. Ma, J. Tian, J. Ma, and D. Zhang, "A robust method for vector field learning with application to mismatch removing," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2011, pp. 2977-2984.
68. S. Zhu, K. Yu, and Y. Gong, "Predictive matrix-variate t models," in Advances in Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 2008, pp. 1721-1728.