  We propose a novel probabilistic method for detection of objects in noisy
images. The method uses results from percolation and random graph theories. We
present an algorithm that allows to detect objects of unknown shapes in the
presence of random noise. Our procedure substantially differs from
wavelets-based algorithms. The algorithm has linear complexity and exponential
accuracy and is appropriate for real-time systems. We prove results on
consistency and algorithmic complexity of our procedure.
