Methods and Theory : Congealing

Overview

Congealing is an algorithm for the joint alignment of a set of images, volumes, or other types of signals. This notion of "alignment" can be construed very broadly. It includes various types of spatial alignment, aligning images under, say, rotation and translation. But it can also refer to the removal of other forms of non-spatial variability, like brightness transformations, that make a set of images different.


We have applied congealing in many different domains, including the following:


Faculty


Graduate Students


Collaborators


Publications

  • Marwan Mattar, Michael Ross, and Erik Learned-Miller
    Nonparametric Curve Alignment.
    Intl. Conference on Acoustics, Speech and Signal Processing, 2009.
    [pdf]
  • Gary B. Huang, Vidit Jain, and Erik Learned-Miller.
    Unsupervised joint alignment of complex images.
    International Conference on Computer Vision (ICCV), 2007.
    [pdf]
  • Erik Learned-Miller.
    Data driven image models through continuous joint alignment.
    In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 28:2, pp. 236-250, 2006.
    [pdf]
  • Erik Miller, Nick Matsakis, and Paul Viola.
    Learning from one example through shared densities on transforms.
    Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Volume 1, pp. 464-471, 2000.
    [pdf]
  • Lilla Zollei, Erik Learned-Miller, Eric Grimson, and William Wells.
    Efficient population registration of 3D data.
    In Workshop on Computer Vision for Biomedical Image Applications: Current Techniques and Future Trends, at the International Conference of Computer Vision (best paper award), 2005.
    [pdf]
  • Erik Learned-Miller and Vidit Jain.
    Many heads are better than one: Jointly removing bias from multiple MRs using nonparametric maximum likelihood.
    In Proceedings of Information Processing in Medical Imaging, pp. 615-626, 2005.
    [pdf]
  • Erik Learned-Miller and Parvez Ahammad.
    Joint MRI bias removal using entropy minimization across images.
    In Neural Information Processing Systems (NIPS) 17, pp. 761-768, 2005.
    [pdf]
  • Chris Stauffer, Erik Miller and Kinh Tieu.
    Transform-invariant image decomposition with similarity templates.
    In Neural Information Processing Systems (NIPS) 14, pp. 1295-1302, 2002.
    [pdf]