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.
