Abstract
Robust Point Correspondence for image registration is still a challenging problem in computer vision and many of its related applications. It is a computationally intensive task which requires an expensive search process especially when issues of noisy and outlying data have to be considered. In this paper, we cast the problem as a combinatorial optimization task and we solve it using extremal optimization, a new general purpose heuristic recently proposed by Boettcher and colleagues. We show how this heuristic has been tailored to the point correspondence problem and resulted in an efficient outlier removal scheme. Experimental results are very encouraging and demonstrate the ability of the proposed method in identifying outliers and achieving robust matching.
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References
S. Boetthcher and A. G. Percus. Nature’s way of optimizing. Artificial Intelligence, 119:275–286, 2000.
L. G. Brown. A survey of image registration techniques. ACM surveys, 24(4): 325–376, December 1992.
S. Banerjee and D. Dutta Majumdar. Shape matching in multimodal medical images using point landmarks with Hopfield net. Neurocomputing, 30(2000): 103–116, 2000.
S. Gold and al. New algorithms for 2D and 3D point matching: Pose estimation and Correspondence. Pattern Recognition, 31(8): 1019–1031, 1998.
D. M. Mount, N. S. Netanyahu and J. Le Moigne. Efficient algorithms for robust feature matching. Pattern Recognition, 32(1): 17–38, January 1999.
X. Dai and S. Khorram. A Feature-Based Image Registration Algorithm Using Improved Chain-Code Representation Combined with Invariant Moments. IEEE Transactions on Geoscience and Remote Sensing, 37(5):2351–2362, Septembre 1999.
E. Guest, E. Berry, R. A. Baldock, M. Fidrich and M. A. Smith. Robust Point Correspondence Applied to Two-and Three-Dimensional Image Registration. IEEE Transactions on PAMI, 23(2):165–179, February 2001.
P. Bak and K. Sneppen. Punctuated equilibrium and criticality in a simple model of evolution. Physical Review letters, 71:4083–4086, 1993.
P. Bak. How nature works: The science of Self-Organized Criticality. New York, NY: Copernicus Press, 1996.
S. Boetthcher and A. G. Percus. Optimization with extremal dynamics. Physical Review letters, 86:5211–5214, June 2001.
S. Boetthcher and A. G. Percus. Extremal optimization for graph partitioning. Physical Review E, 64(026114):1–13, July 2001.
C. Harris and M. Stephens. A combined corner and edge detector, Proceedings of the Fourth Alvey Vision Conference, pp. 147–151, 1988.
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© 2002 Springer-Verlag Berlin Heidelberg
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Meshoul, S., Batouche, M. (2002). Robust Point Correspondence for Image Registration Using Optimization with Extremal Dynamics. In: Van Gool, L. (eds) Pattern Recognition. DAGM 2002. Lecture Notes in Computer Science, vol 2449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45783-6_40
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DOI: https://doi.org/10.1007/3-540-45783-6_40
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