  We develop an unsupervised, nonparametric, and scalable statistical learning
method for detection of unknown 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 and sizes in the
presence of nonparametric noise of unknown level. The noise density is assumed
to be unknown and can be very irregular. The algorithm has linear complexity
and exponential accuracy and is appropriate for real-time systems. We prove
strong consistency and scalability of our method in this setup with minimal
assumptions.
