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  Robust nonparametric detection of objects in noisy images

Langovoy, M., & Wittich, O.(2010). Robust nonparametric detection of objects in noisy images (2010-049). Eindhoven, The Netherlands: EURANDOM.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BE7C-D Version Permalink: http://hdl.handle.net/21.11116/0000-0002-95A6-A
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 Creators:
Langovoy, M1, Author              
Wittich, O, Author
Affiliations:
1Technische Universiteit Eindhoven, ou_persistent22              

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 Abstract: 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 im- portant 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 nite sample performance of our test.

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 Dates: 2010-09
 Publication Status: Published in print
 Pages: 21
 Publishing info: Eindhoven, The Netherlands : EURANDOM
 Table of Contents: -
 Rev. Method: -
 Identifiers: Report Nr.: 2010-049
BibTex Citekey: LangovoyW2010_2
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Title: Eurandom Preprint Series
Source Genre: Series
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Pages: - Volume / Issue: 2010-049 Sequence Number: - Start / End Page: 1 - 21 Identifier: ISSN: 1389-2355