Projective image alignment by using ECC maximization

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.