Unsupervised nonparametric detection of unknown objects in noisy images based on percolation theory
Abstract
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.
Cite
@article{arxiv.1102.5019,
title = {Unsupervised nonparametric detection of unknown objects in noisy images based on percolation theory},
author = {Mikhail A. Langovoy and Olaf Wittich and Patrick Laurie Davies},
journal= {arXiv preprint arXiv:1102.5019},
year = {2018}
}
Comments
Added references, updated terminology, changed the order of the authors and the title. arXiv admin note: substantial text overlap with arXiv:1102.4803, arXiv:1102.5014