Issue No. 10 - October (2008 vol. 30)
Georgios D. Evangelidis , University of Patras, GR 26500 Patras
Emmanouil Z. Psarakis , University of Patras, GR 26500 Patras
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.
Image Processing and Computer Vision, Registration, Motion, Gradient methods
E. Z. Psarakis and G. D. Evangelidis, "Parametric Image Alignment Using Enhanced Correlation Coefficient Maximization," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 30, no. , pp. 1858-1865, 2008.