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Adaptive nonparametric detection in cryo-electron microscopy

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84039

Langovoy,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83949

Habeck,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Langovoy, M., Habeck, M., & Schölkopf, B. (2011). Adaptive nonparametric detection in cryo-electron microscopy. In 58th World Statistics Congress of the International Statistical Institute (ISI 2011) (pp. 1-6).


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-BACC-C
Zusammenfassung
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 multiple objects of unknown shapes in the case of nonparametric noise. The noise density is unknown and can be heavy-tailed. The objects of interest have unknown varying intensities. No boundary shape constraints are imposed on the objects, only a set of weak bulk conditions is required. We view the object detection problem as hypothesis testing for discrete statistical inverse problems. We present an algorithm that allows to detect greyscale objects of various shapes in noisy images. We prove results on consistency and algorithmic complexity of our procedures. Applications to cryo-electron microscopy are presented.