Analysis of descent-based image registration

SIAM Journal on Imaging Sciences

Volumn 6, Issue 4, 2013, Pages 2310-2349

  Vural, Elif ^a   Frossard, Pascal ^a  

^a EPFL   (Switzerland)

Author keywords

Gradient descent; Hierarchical registration methods; Image registration; Image smoothing; Performance analysis

Indexed keywords

GRADIENT DESCENT; HIERARCHICAL REGISTRATION; HIERARCHICAL TECHNIQUES; IMAGE SMOOTHING; MULTISCALE REGISTRATIONS; OBJECTIVE FUNCTIONS; PERFORMANCE ANALYSIS; REGISTRATION ACCURACY;

FUNCTIONS;

IMAGE REGISTRATION;

EID: 84891109658     PISSN: None     EISSN: 19364954     Source Type: Journal    
DOI: 10.1137/130909858     Document Type: Article

References (34)

1. L. Alvarez, J. Weickert, and J. Sánchez, A scale-space approach to nonlocal optical flow calculations, in Proceedings of the 2nd International Conference on Scale-Space Theories in Computer Vision, SCALE-SPACE '99, Springer-Verlag, London, 1999, pp. 235-246.
2. P. Anandan, A computational framework and an algorithm for the measurement of visual motion, Int. J. Comput. Vis., 2 (1989), pp. 283-310.
3. J. Antoine, R. Murenzi, P. Vandergheynst, and S. Ali, Two-Dimensional Wavelets and Their Relatives, Cambridge University Press, Cambridge, UK, 2004.
4. J. L. Barron, D. J. Fleet, and S. S. Beauchemin, Performance of optical flow techniques, Int. J. Comput. Vis., 12 (1994), pp. 43-77.
5. J. R. Bergen, P. Anandan, K. J. Hanna, and R. Hingorani, Hierarchical model-based motion estimation, in Computer Vision-ECCV'92, Springer-Verlag, London, 1992, pp. 237-252.
6. J. W. Brandt, Analysis of bias in gradient-based optical flow estimation, in Proceedings of the 1994 Conference Records of the 28th Asilomar Conference on Signals, Systems and Computers, Vol. 1, 1994, pp. 721-725.
7. L. G. Brown, A survey of image registration techniques, ACM Comput. Surveys, 24 (1992), pp. 325-376.
8. C. Cafforio and F. Rocca, The differential method for image motion estimation, in Image Sequence Processing and Dynamic Scene Analysis, Springer-Verlag, Berlin, 1983, pp. 104-124.
9. A. W. Fitzgibbon and A. Zisserman, Joint manifold distance: A new approach to appearance based clustering, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Vol. 1, 2003, pp. 26-36.
10. J. K. Kearney, W. B. Thompson, and D. L. Boley, Optical flow estimation: An error analysis of gradient-based methods with local optimization, IEEE Trans. Pattern Anal. Mach. Intell., 9 (1987), pp. 229-244.
11. M. Lefébure and L.D. Cohen, Image registration, optical flow and local rigidity, J. Math. Imaging Vision, 14 (2001), pp. 131-147.
12. T. Lindeberg, Scale-Space Theory in Computer Vision, Kluwer Academic Publishers, Dordrecht, The Netherlands, 1994.
13. B. D. Lucas and T. Kanade, An iterative image registration technique with an application to stereo vision, in Proceedings of the 7th International Joint Conference on Artificial Intelligence, 1981, pp. 674-679.
14. J. Maintz and M. Viergever, A survey of medical image registration, Med. Image Anal., 2 (1998), pp. 1-36.
15. S. G. Mallat and Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Trans. Signal Process., 41 (1993), pp. 3397-3415.
16. E. Mémin and P. Pérez, Hierarchical estimation and segmentation of dense motion fields, Int. J. Comput. Vis., 46 (2002), pp. 129-155.
17. O. Nestares and D. J. Heeger, Robust multiresolution alignment of MRI brain volumes, Magn. Reson. Med., 43 (2000), pp. 705-715.
18. A. N. Netravali and J. D. Robbins, Motion-compensated television coding: Part I, Bell System Tech. J., 58 (1979), pp. 631-670.
19. Y. C. Pati, R. Rezaiifar, and P. S. Krishnaprasad, Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition, in Proceedings of the 1993 Conference Record of the 27th Asilomar Conference on Signals, Systems and Computers, 1993, pp. 40-44.
20. T. Q. Pham, M. Bezuijen, L. J. van Vliet, K. Schutte, and C. L. Luengo, Performance of optimal registration estimators, in Visual Information Processing XIV, Proc. SPIE 5817, SPIE, Bellingham, WA, 2005, pp. 133-144.
21. D. Robinson and P. Milanfar, Fundamental performance limits in image registration, IEEE Trans. Image Process., 13 (2004), pp. 1185-1199.
22. N. Sabater, J.-M. Morel, and A. Almansa, How accurate can block matches be in stereo vision?, SIAM J. Imaging Sci., 4 (2011), pp. 472-500.
23. P. Simard, Y. LeCun, J. S. Denker, and B. Victorri, Transformation invariance in pattern recognition-tangent distance and tangent propagation, in Neural Networks: Tricks of the Trade, Springer-Verlag, New York, 1998, pp. 235-269.
24. G. Tziritas and C. Labit, Motion Analysis for Image Sequence Coding, Elsevier Science, New York, 1994.
25. N. Vasconcelos and A. Lippman, A multiresolution manifold distance for invariant image similarity, IEEE Trans. Multimed., 7 (2005), pp. 127-142.
26. E. Vural and P. Frossard, Analysis of Descent-Based Image Registration, EPFL-REPORT-183845, 2013; available online from http://infoscience.epfl.ch/record/183845.
27. E. Vural and P. Frossard, Analysis of hierarchical image alignment with descent methods, in Proceedings of the 10th International Conference on Sampling Theory and Applications, 2013.
28. E. Vural and P. Frossard, Learning smooth pattern transformation manifolds, IEEE Trans. Image Process., 22 (2013), pp. 1311-1325.
29. D. Walker and K. Rao, Improved pel-recursive motion compensation, IEEE Trans. Comm., 32 (1984), pp. 1128-1134.
30. M. P. Wand and M. C. Jones, Kernel Smoothing, Chapman and Hall/CRC, London, 1995.
31. A. P. Witkin, Scale-space filtering., in Proceedings of the 8th International Joint Conference on Artificial Intelligence, Vol. 2, Karlsruhe, Germany, 1983, pp. 1019-1022.
32. M. Xu, H. Chen, and P. K. Varshney, Ziv-Zakai bounds on image registration, IEEE Trans. Signal Process., 57 (2009), pp. 1745-1755.
33. İ. Ş. Yetik and A. Nehorai, Performance bounds on image registration, IEEE Trans. Signal Process., 54 (2006), pp. 1737-1749.
34. B. Zitová and J. Flusser, Image registration methods: A survey, Image Vis. Comput., 21 (2003), pp. 977-1000.