@MastersThesis{Svoboda-TR-2007-11,
  UPDATE  = { 2007-07-24 },
  author =       {Svoboda, Ji{\v r}{\'\i}},
  supervisor =   {Kybic, Jan},
  language =     {czech},
  title =        {Porovn{\'a}n{\'\i} metod odhadov{\'a}n{\'\i} 
                  entropie pro registraci obraz\accent23u},
  e_title =      {Entropy estimation methods comparison for image
                  registration},
  school =       {Center for Machine Perception, 
                  K13133 FEE Czech Technical University},
  address =      {Prague, Czech Republic},
  year =         {2007},
  month =        {May},
  day =          {19},
  type =         {{MSc Thesis CTU--CMP--2007--11}},
  issn =         {1213-2365},
  pages =        {126},
  figures =      {43},
  psurl =        {[PDF, 1.3MB]},
  url =  {ftp://cmp.felk.cvut.cz/pub/cmp/articles/kybic/Svoboda-MSthesis2007.pdf},
  project =      {},
  annote = {Image registration is an important field of computer
    vision. In the last decade, methods using mutual information
    between registrered images as their similarity criterion have been
    gaining popularity. The aim of this work is to evaluate
    statistical properties and speed of certain entropy and mutual
    information estimator implementations on data of various
    dimensionalities and probability distributions. Among the
    estimators evaluated are: the histogram estimator, in its
    classical form and with enhancements such as histogram smoothing
    and adaptive binning, entropy and mutual information estimator
    based on kernel density estimation and a nearest-neighbor based
    estimator and its faster modifications replacing nearest-neighbors
    with approximate nearest-neighbors. Also, we assess statistical
    properties of an Renyi entropy estimator based on the length of a
    minimum spanning tree spanning the samples.},
  keywords =     {entropy estimation, image registration},
}