Home/Lucas-Kanade 20 Years On: A Unifying Framework: Part 1

Lucas-Kanade 20 Years On: A Unifying Framework: Part 1

Simon Baker and Iain Matthews
Tech. Report, CMU-RI-TR-02-16, Robotics Institute, Carnegie Mellon University, July, 2002

View Publication

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.


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-ing, 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 of a 2 part series, we cover the quantity approximated, the warp update rule, and the gradient descent approximation. In a future Part 2 of this 2 paper series we will cover the choice of the norm, how to allow linear appearance variation, how to impose priors on the parameters, and various heuristics to avoid local minima.

author = {Simon Baker and Iain Matthews},
title = {Lucas-Kanade 20 Years On: A Unifying Framework: Part 1},
year = {2002},
month = {July},
institution = {Carnegie Mellon University},
address = {Pittsburgh, PA},
number = {CMU-RI-TR-02-16},
keywords = {Image alignment, Lucas-Kanade, a unifying framework, additive vs. compositional algorithms, forwards vs. inverse algorithms, the inverse compositional algorithm, efficiency, steepest descent, Gauss-Newton, Newton, Levenberg-Marquardt},
} 2017-09-13T10:45:06-04:00