# PROJECTIVE IMAGE ALIGNMENT BY USING ECC MAXIMIZATION

### Georgios D. Evangelidis, Emmanouil Z. Psarakis

#### Abstract

Nonlinear projective transformation provides the exact number of desired parameters to account for all possible camera motions thus making its use in problems where the objective is the alignment of two or more image profiles to be considered as a natural choice. Moreover, the ability of an alignment algorithm to quickly and accurately estimate the parameter values of the geometric transformation even in cases of over-modelling of the warping process constitutes a basic requirement to many computer vision applications. In this paper the appropriateness of the Enhanced Correlation Coefficient (ECC) function as a performance criterion in the projective image registration problem is investigated. Since this measure is a highly nonlinear function of the warp parameters, its maximization is achieved by using an iterative technique. The main theoretical results concerning the nonlinear optimization problem and an efficient approximation leading to an optimal closed form solution (per iteration) are presented. The performance of the iterative algorithm is compared against the well known Lucas-Kanade algorithm with the help of a series of experiments involving strong or weak geometric deformations, ideal and noisy conditions and even over-modelling of the warping process. In all cases ECC based algorithm exhibits a better behavior in speed, as well as in the probability of convergence as compared to the Lucas-Kanade scheme.

#### References

- Anandan, P. (1989). A computational framework and an algorithm for the measurement of visual motion. International Journal of Computer Vision, 2(3):283-310.
- Baker, S. and Matthews, I. (2004). Lucas-kanade 20 years on: A unifying framework: Part 1: The quantity approximated, the warp update rule, and the gradient descent approximation. volume 56, pages 221-255.
- Evangelidis, G. D. and Psarakis, E. Z. (2007). Parametric image alignment using enhanced correlation coefficient maximization. Submitted to IEEE Trans. on PAMI, submission TPAMI-0026-0107.
- Fuh, C. and Maragos, P. (1991). Motion dislpacement estimation using an affine model for image matching. Optical Engineering, 30(7):881-887.
- Gleicher, M. (1997). Projective registration with difference decomposition. In Proc. of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR'97). San Juan, Puerto Rico.
- Hager, G. D. and Belhumeur, P. N. (1998). Efficient region tracking with parametric models of geometry and illumination. IEEE Trans. on PAMI, 20(10):1025-1039.
- Johnson, H. and Christensen, G. (2002). Consistent landmark and intensity-based image registration. IEEE Transactions on Medical Imaging, 21(5):450-461.
- Lucas, B. D. and Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. In Proc. of 7th International Joint Conf on Artificial Intelligence (IJCAI). Vancouver, British Columbia.
- Psarakis, E. Z. and Evangelidis, G. D. (2005). An enhanced correlation-based method for stereo correspondence with sub-pixel accuracy. In Proc. of 10th IEEE International Conference on Computer Vision (ICCV 2005). Beijing, China.
- Radke, R., Ramadge, P., Echigo, T., and Iisaku, S. (2000). Efficiently estimating projective transformations. In Proc. of IEEE International Conference on Image Processing (ICIP'00), pages 232-235. Vancouver,Canada.
- Shum, H. and Szeliski, R. (2000). Construction of panoramic image mosaics with global and local alignment. International Journal on Computer Vision, 36(2):101-130.
- Szeliski, R. (2005). Handbook of Mathematical Models of Computer Vision. Springer. ch. 17.
- Szeliski, R. (2006). Image alignment and stitching: A tutorial. Foundations and Trends in Computer Graphics and Computer Vision, 2(1):1-104.

#### Paper Citation

#### in Harvard Style

Evangelidis G. and Psarakis E. (2008). **PROJECTIVE IMAGE ALIGNMENT BY USING ECC MAXIMIZATION** . In *Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)* ISBN 978-989-8111-21-0, pages 413-420. DOI: 10.5220/0001087204130420

#### in Bibtex Style

@conference{visapp08,

author={Georgios D. Evangelidis and Emmanouil Z. Psarakis},

title={PROJECTIVE IMAGE ALIGNMENT BY USING ECC MAXIMIZATION},

booktitle={Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)},

year={2008},

pages={413-420},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0001087204130420},

isbn={978-989-8111-21-0},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)

TI - PROJECTIVE IMAGE ALIGNMENT BY USING ECC MAXIMIZATION

SN - 978-989-8111-21-0

AU - Evangelidis G.

AU - Psarakis E.

PY - 2008

SP - 413

EP - 420

DO - 10.5220/0001087204130420