Detection of objects in noisy images and site percolation on square lattices

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.

This paper first appeared as EURANDOM Report 2009-035 on November 11, 2009. Link to the paper at the EURANDOM repository: http://www.eurandom.tue.nl/reports/2009/035-report.pdf Link to the abstract at EURANDOM repository: http://www.eurandom.tue.nl/reports/2009/035-abstract.pdf