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Issue No.10 - October (2008 vol.30)
pp: 1858-1865
Georgios D. Evangelidis , University of Patras, GR 26500 Patras
Emmanouil Z. Psarakis , University of Patras, GR 26500 Patras
ABSTRACT
In this work we propose the use of a modified version of the correlation coefficient as a performance criterion for the image alignment problem. The proposed modification has the desirable characteristic of being invariant with respect to photometric distortions. Since the resulting similarity measure is a nonlinear function of the warp parameters, we develop two iterative schemes for its maximization, one based on the forward additive approach and the second on the inverse compositional method. As it is customary in iterative optimization, in each iteration the nonlinear objective function is approximated by an alternative expression for which the corresponding optimization is simple. In our case we propose an efficient approximation that leads to a closed form solution (per iteration) which is of low computational complexity, the latter property being particularly strong in our inverse version. The proposed schemes are tested against the Forward Additive Lucas-Kanade and the Simultaneous Inverse Compositional algorithm through simulations. Under noisy conditions and photometric distortions our forward version achieves more accurate alignments and exhibits faster convergence whereas our inverse version has similar performance as the Simultaneous Inverse Compositional algorithm but at a lower computational complexity.
INDEX TERMS
Image Processing and Computer Vision, Registration, Motion, Gradient methods
CITATION
Georgios D. Evangelidis, Emmanouil Z. Psarakis, "Parametric Image Alignment Using Enhanced Correlation Coefficient Maximization", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.30, no. 10, pp. 1858-1865, October 2008, doi:10.1109/TPAMI.2008.113
REFERENCES
[1] S. Periaswamy and H. Farid, “Elastic Registration in the Presence of Intensity Variation,” IEEE Trans. Medical Imaging, vol. 22, no. 7, pp. 865-874, 2003.
[2] I. Karybali, E.Z. Psarakis, K. Berberidis, and G.D. Evangelidis, “Efficient Image Registration with Subpixel Accuracy,” Proc. 14th European Signal Processing Conf., 2006.
[3] G.D. Hager and P.N. Belhumeur, “Efficient Region Tracking with Parametric Models of Geometry and Illumination,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 10, pp. 1025-1039, Oct. 1998.
[4] J. Shi and C. Tomasi, “Good Features to Track,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 1994.
[5] M. Gleicher, “Projective Registration with Difference Decomposition,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 1997.
[6] C. Fuh and P. Maragos, “Motion Displacement Estimation Using an Affine Model for Image Matching,” Optical Eng., vol. 30, no. 7, pp. 881-887, 1991.
[7] Y. Altunbasak, R.M. Mersereau, and A.J. Patti, “A Fast Parametric Motion Estimation Algorithm with Illumination and Lens Distortion Correction,” IEEE Trans. Image Processing, vol. 12, no. 4, pp. 395-408, 2003.
[8] B.K.P. Horn and E.J. Weldon, “Direct Methods for Recovering Motion,” Int'l J. Computer Vision, vol. 2, no. 1, pp. 51-76, 1988.
[9] P. Anandan, “A Computational Framework and an Algorithm for the Measurement of Visual Motion,” Int'l J. Computer Vision, vol. 2, no. 3, pp.283-310, 1989.
[10] B.D. Lucas and T. Kanade, “An Iterative Image Registration Technique with an Application to Stereo Vision,” Proc. Seventh Int'l Joint Conf. Artificial Intelligence, 1981.
[11] E.Z. Psarakis and G.D. Evangelidis, “An Enhanced Correlation-Based Method for Stereo Correspondence with Sub-Pixel Accuracy,” Proc. 10th IEEE Int'l Conf. Computer Vision, 2005.
[12] R. Szeliski, Handbook of Mathematical Models of Computer Vision, N. Paragios, Y. Chen, and O. Faugeras, eds., chapter 17. Springer, 2005.
[13] S. Baker and I. Matthews, “Lucas-Kanade 20 Years On: A Unifying Framework: Part 1. The Quantity Approximated, the Warp Update Rule, and the Gradient Descent Approximation,” Int'l J. Computer Vision, vol. 56, no. 3, pp. 221-255, 2004.
[14] M.J. Black and Y. Yacoob, “Tracking and Recognizing Rigid and Non-Rigid Facial Motions Using Local Parametric Models of Image Motion,” Proc. Fifth IEEE Int'l Conf. Computer Vision, 1995.
[15] H. Shum and R. Szeliski, “Construction of Panoramic Image Mosaics with Global and Local Alignment,” Int'l J. Computer Vision, vol. 36, no. 2, pp. 101-130, 2000.
[16] S. Nagahdaripour and C.H. Yu, “A Generalized Brightness Change Model for Computing Optical Flow,” Proc. Fourth IEEE Int'l Conf. Computer Vision, 1993.
[17] B.K.P. Horn and B.G. Schunk, “Determining Optical Flow,” Artificial Intelligence, vol. 17, pp. 185-203, 1981.
[18] M.J. Black and P. Anandan, “A Framework for the Robust Estimation of Optical Flow,” Proc. Fourth IEEE Int'l Conf. Computer Vision, 1993.
[19] S. Baker, R. Gross, and I. Matthews, “Lucas-Kanade 20 Years On: A Unifying Framework: Part 3,” CMU-RI-TR-03-35, Robotics Inst., Carnegie Mellon Univ., 2004.
[20] B.K.P. Horn, Robot Vision. MIT Press, McGraw-Hill, 1986.
[21] P. Hallinan, “A Low-Dimensional Representation of Human Faces for Arbitrary Lighting Conditions,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 1994.
[22] V. Barnett and T. Lewis, Outliers in Statistical Data. John Wiley & Sons, 1978.
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