IS = { zkontrolovano 24 Jan 2011 },
  UPDATE  = { 2009-12-29 },
  author = {Kybic, Jan},
  title  = {Bootstrap Resampling for Image Registration Uncertainty 
            Estimation without Ground Truth},
  publisher = {Institute of Electrical and Electronics Engineers},
  address   = {445 Hoes Lane, Piscataway, USA},
  issn   = {1057-7149},
  pages  = {64--73},
  month  = {January},
  volume = {19},
  number = {1},
  annote = {We address the problem of estimating the uncertainty of
    pixel based image registration algorithms, given just the two
    images to be registered, for cases when no ground truth data is
    available. Our novel method uses bootstrap resampling. It is very
    general, applicable to almost any registration method based on
    minimizing a pixel-based similarity criterion; we demonstrate it
    using the SSD, SAD, correlation, and mutual information
    criteria. We show experimentally that the bootstrap method
    provides better estimates of the registration accuracy than the
    state-of-the-art Cramer-Rao bound method. Additionally, we
    evaluate also a fast registration accuracy estimation (FRAE)
    method which is based on quadratic sensitivity analysis ideas and
    has a negligible computational overhead. FRAE mostly works better
    than the Cramer-Rao bound method but is outperformed by the
    bootstrap method.},
  keywords = {bootstrap, image registration, uncertainty estimation},
  journal  = {IEEE Transactions on Image Processing},
  url = {ftp://cmp.felk.cvut.cz/pub/cmp/articles/kybic/Kybic-ieeeTIP2009.pdf},
  project = {1M0567},
  year = {2010},