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Abstract:
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.
