Computing accurate correspondences across groups of images

Timothy F. Cootes, Carole J. Twining, Vladimir S. Petrović, Kolawole O. Babalola, Christopher J. Taylor

    Research output: Contribution to journalArticlepeer-review


    Groupwise image registration algorithms seek to establish dense correspondences between sets of images. Typically, they involve iteratively improving the registration between each image and an evolving mean. A variety of methods have been proposed, which differ in their choice of objective function, representation of deformation field, and optimization methods. Given the complexity of the task, the final accuracy is significantly affected by the choices made for each component. Here, we present a groupwise registration algorithm which can take advantage of the statistics of both the image intensities and the range of shapes across the group to achieve accurate matching. By testing on large sets of images (in both 2D and 3D), we explore the effects of using different image representations and different statistical shape constraints. We demonstrate that careful choice of such representations can lead to significant improvements in overall performance. © 2006 IEEE.
    Original languageEnglish
    Article number5342433
    Pages (from-to)1994-2005
    Number of pages11
    JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
    Issue number11
    Publication statusPublished - 2010


    • appearance models
    • correspondence problem
    • Nonrigid registration


    Dive into the research topics of 'Computing accurate correspondences across groups of images'. Together they form a unique fingerprint.

    Cite this