@techreport{LangovoyW2010_2,
  title = {Robust nonparametric detection of objects in noisy images},
  abstract = {We propose a novel statistical hypothesis testing method for detection of objects
  in noisy images. The method uses results from percolation theory and random graph theory.
  We present an algorithm that allows to detect objects of unknown shapes in the presence of
  nonparametric noise of unknown level and of unknown distribution. No boundary shape constraints
  are imposed on the object, only a weak bulk condition for the object's interior is required. The
  algorithm has linear complexity and exponential accuracy and is appropriate for real-time systems.
  In this paper, we develop further the mathematical formalism of our method and explore im-
  portant connections to the mathematical theory of percolation and statistical physics. We prove
  results on consistency and algorithmic complexity of our testing procedure. In addition, we address
  not only an asymptotic behavior of the method, but also a nite sample performance of our test.},
  number = {2010-049},
  institution = {EURANDOM, Technische Universiteit Eindhoven},
  month = sep,
  year = {2010},
  slug = {langovoyw2010_2},
  author = {Langovoy, M. and Wittich, O.},
  month_numeric = {9}
}