Robust Image alignment using third-order global motion estimation

Y. Keller, A. Averbuch

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The estimation of parametric global motion using non-linear optimization is a fundamental technique in computer vision. Such schemes are able to recover various motion models (translation, rotation, affine, projective) with subpixel accuracy. The parametric motion is computed using a first order Taylor expansions of the registered images. But, it is limited to the estimation of small motions, and while large translations and rotations can be coarsely estimated by Fourier domain algorithms, no such techniques exist for affine and projective motions. This paper offers two contributions: First, we improve the convergence properties by an order of magnitude using a second order Taylor expansion. A third order convergence rate is achieved, compared to the second order convergence of prior schemes. Second, we extend the third order algorithm using a symmetrical formulation which further improves the convergence properties. The results are verified by rigorous analysis and experimental trials.
Original languageAmerican English
Title of host publicationBritish Machine Vision Conference (BMVC)
StatePublished - 2005

Bibliographical note

Place of conference:UK


Dive into the research topics of 'Robust Image alignment using third-order global motion estimation'. Together they form a unique fingerprint.

Cite this