@techreport{LangovoyW2010_3,
  title = {Computationally efficient algorithms for statistical image processing: Implementation in R},
  abstract = {In the series of our earlier papers on the subject, we proposed a novel statistical hy-
  pothesis testing method for detection of objects in noisy images. The method uses results from
  percolation theory and random graph theory. We developed algorithms that allowed to detect
  objects of unknown shapes in the presence of nonparametric noise of unknown level and of un-
  known distribution. No boundary shape constraints were imposed on the objects, only a weak
  bulk condition for the object's interior was required. Our algorithms have linear complexity and
  exponential accuracy. In the present paper, we describe an implementation of our nonparametric
  hypothesis testing method. We provide a program that can be used for statistical experiments in
  image processing. This program is written in the statistical programming language R.},
  number = {2010-053},
  institution = {EURANDOM, Technische Universiteit Eindhoven},
  month = dec,
  year = {2010},
  author = {Langovoy, M. and Wittich, O.},
  month_numeric = {12}
}