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Robust nonparametric detection of objects in noisy images

By Mikhail A. Langovoy and Olaf Wittich


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 important 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 finite sample performance of our test.Comment: This paper initially appeared in 2010 as EURANDOM Report 2010-049. Link to the abstract at EURANDOM repository: Link to the paper at EURANDOM repository:

Topics: Mathematics - Statistics Theory, Mathematical Physics, Mathematics - Probability, Statistics - Applications, Statistics - Methodology
Year: 2011
DOI identifier: 10.1080/10485252.2012.759570
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