Article

Parametric Image Alignment Using Enhanced Correlation Coefficient Maximization

Signal Processing and Communications Lab, Department of Computer Engineering and Informatics, University of Patras, Rio-Patras, Greece.
IEEE Transactions on Pattern Analysis and Machine Intelligence (Impact Factor: 5.69). 11/2008; 30(10):1858-65. DOI: 10.1109/TPAMI.2008.113
Source: PubMed

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 (SIC) 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 SIC algorithm but at a lower computational complexity.

Download full-text

Full-text

Available from: Emmanouil Psarakis, Jul 29, 2015
1 Follower
 · 
158 Views
  • Source
    • "However, different recording times come with variant illumination and outliers. To handle the former we extend in time the recently proposed ECC image alignment algorithm [6] that offers robustness to appearance variation. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Video synchronization and alignment is a rather recent topic in computer vision. It usually deals with the problem of aligning sequences recorded simultaneously by static, jointly- or independently-moving cameras. In this paper, we investigate the more difficult problem of matching videos captured at different times from independently-moving cameras, whose trajectories are approximately coincident or parallel. To this end, we propose a novel method that pixel-wise aligns videos and allows thus to automatically highlight their differences. This primarily aims at visual surveillance but the method can be adopted as is by other related video applications, like object transfer (augmented reality) or high dynamic range video. We build upon a slice matching scheme to first synchronize the sequences, while we develop a spatio-temporal alignment scheme to spatially register corresponding frames and refine the temporal mapping. We investigate the performance of the proposed method on videos recorded from vehicles driven along different types of roads and compare with related previous works.
    IEEE International Conference on Computer Vision Workshops, ICCV 2011 Workshops, Barcelona, Spain, November 6-13, 2011; 11/2011
  • Source
    • "we have dropped the dependence of the quantities on p for notational simplicity. Finally, the maximization of (19) with respect to ∆p can be obtained by applying the results of [14]. In particular, the maximum value is attained for "
    [Show abstract] [Hide abstract]
    ABSTRACT: We propose a correlation-based approach to parametric object alignment particularly suitable for face analysis applications which require efficiency and robustness against occlusions and illumination changes. Our algorithm registers two images by iteratively maximizing their correlation coefficient using gradient ascent. We compute this correlation coefficient from complex gradients which capture the orientation of image structures rather than pixel intensities. The maximization of this gradient correlation coefficient results in an algorithm which is as computationally efficient as ℓ2 norm-based algorithms, can be extended within the inverse compositional framework (without the need for Hessian re-computation) and is robust to outliers. To the best of our knowledge, no other algorithm has been proposed so far having all three features. We show the robustness of our algorithm for the problem of face alignment in the presence of occlusions and non-uniform illumination changes. The code that reproduces the results of our paper can be found at http://ibug.doc.ic.ac.uk/resources.
    IEEE International Conference on Computer Vision, ICCV 2011, Barcelona, Spain, November 6-13, 2011; 01/2011
  • Source
    • "Since it is natural to prefer an algorithm that converges quickly with high probability, we propose a third figure of merit that captures exactly this point (Evangelidis and Psarakis, 2007). In other words we propose the generation of a histogram depicting the probability of successful convergence at each iteration . "
    [Show abstract] [Hide abstract]
    ABSTRACT: Image alignment, image registration, motion estimation, parametric motion, image matching, mosaic construction, gradient methods, correlation coefficient. Nonlinear projective transformation provides the exact number of desired parameters to account for all possible camera motions thus making its use a natural choice in image alignment problems. 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 for 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 by using an iterative technique is achieved. The main theoretical results concerning the nonlinear optimization problem and an efficient approximation leads 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 through 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. 1
    VISAPP 2008: Proceedings of the Third International Conference on Computer Vision Theory and Applications, Funchal, Madeira, Portugal, January 22-25, 2008 - Volume 1; 01/2008
Show more