Unsupervised nonparametric detection of unknown objects in noisy images based on percolation theory

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 pres...

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Bibliographic Details
Published in:arXiv.org 2018-07
Main Authors: Langovoy, Mikhail A, Wittich, Olaf, Davies, Patrick Laurie
Format: Article
Language:English
Subjects:
Algorithms
Graph theory
Image detection
Object recognition
Percolation theory
Shape recognition
Online Access:Get full text
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Summary: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.
ISSN:2331-8422