International Journal of Computer Vision

, Volume 56, Issue 3, pp 221–255

Lucas-Kanade 20 Years On: A Unifying Framework

  • Simon Baker
  • Iain Matthews

DOI: 10.1023/B:VISI.0000011205.11775.fd

Cite this article as:
Baker, S. & Matthews, I. International Journal of Computer Vision (2004) 56: 221. doi:10.1023/B:VISI.0000011205.11775.fd


Since the Lucas-Kanade algorithm was proposed in 1981 image alignment has become one of the most widely used techniques in computer vision. Applications range from optical flow and tracking to layered motion, mosaic construction, and face coding. Numerous algorithms have been proposed and a wide variety of extensions have been made to the original formulation. We present an overview of image alignment, describing most of the algorithms and their extensions in a consistent framework. We concentrate on the inverse compositional algorithm, an efficient algorithm that we recently proposed. We examine which of the extensions to Lucas-Kanade can be used with the inverse compositional algorithm without any significant loss of efficiency, and which cannot. In this paper, Part 1 in a series of papers, we cover the quantity approximated, the warp update rule, and the gradient descent approximation. In future papers, we will cover the choice of the error function, how to allow linear appearance variation, and how to impose priors on the parameters.

image alignmentLucas-Kanadea unifying frameworkadditive vs. compositional algorithmsforwards vs. inverse algorithmsthe inverse compositional algorithmefficiencysteepest descentGauss-NewtonNewtonLevenberg-Marquardt

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Simon Baker
    • 1
  • Iain Matthews
    • 1
  1. 1.The Robotics InstituteCarnegie Mellon UniversityUSA