Image registration with global and local luminance alignment

  • Jiaya Jia
  • Chi-Keung Tang
  • Jia J
 et al. 
  • 31


    Mendeley users who have this article in their library.
  • 27


    Citations of this article.


Inspired by tensor voting, we present luminance voting, a novel approach for image registration with global and local luminance alignment. The key to our modeless approach is the direct estimation of replacement function, by reducing the complex estimation problem to the robust 2D tensor voting in the corresponding voting spaces. No model for replacement function is assumed. Luminance data are first encoded into 2D ball tensors. Subject to the monotonic constraint only, we vote for an optimal replacement function by propagating the smoothness constraint using a dense tensor field. Our method effectively infers missing curve segments and rejects image outliers without assuming any simplifying or complex curve model. The voted replacement functions are used in our iterative registration algorithm for computing the best warping matrix. Unlike previous approaches, our robust method corrects exposure disparity even if the two overlapping images are initially misaligned. Luminance voting is effective in correcting exposure difference, eliminating vignettes, and thus improving image registration. We present results on a variety of images.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • Jiaya Jia

  • Chi-Keung Tang

  • Jiaya Jia Jiaya Jia

  • Chi-Keung Tang Chi-Keung Tang

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free