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Statistical image analysis and percolation theory




We develop a novel method for detection of signals and reconstruction of images in the presence of random noise. The method uses results from percolation theory. We specifically address the problem of detection of objects of unknown shapes in the case of nonparametric noise. The noise density is unknown and can be heavy-tailed. We view the object detection problem as hypothesis testing for discrete statistical inverse problems. We present an algorithm that allows to detect objects of various shapes in noisy images. We prove results on consistency and algorithmic complexity of our procedures.

Author(s): Davies, PL. and Langovoy, M. and Wittich, O.
Year: 2010
Month: August
Day: 0

Department(s): Empirical Inference
Bibtex Type: Talk (talk)

Digital: 0
Event Name: 73rd Annual Meeting of the Institute of Mathematical Statistics (IMS 2010)
Event Place: Gothenburg, Sweden

Links: Web


  title = {Statistical image analysis and percolation theory},
  author = {Davies, PL. and Langovoy, M. and Wittich, O.},
  month = aug,
  year = {2010},
  month_numeric = {8}