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Inference on binary images from binary data

Part of: Geometric probability and stochastic geometry Inference from stochastic processes

Published online by Cambridge University Press:  01 July 2016

C. A. Glasbey
C. A. Glasbey*
Affiliation:
University of Edinburgh
*
∗ Postal address: Biomathematics and Statistics Scotland, The University of Edinburgh, James Clerk Maxwell Building, King's Buildings, Edinburgh, EH9 3JZ, UK.
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Abstract

The problem addressed is to reverse the degradation which occurs when images are digitised: they are blurred, subjected to noise and rounding error, and sampled only at a lattice of points. Inference is considered for the fundamental case of binary scenes, binary data and isotropic blur. The inferential process is separable into two stages: first from the lattice points to a binary image in continuous space and then the reversal of thresholding and blur. Methods are motivated by, and illustrated using, an electron micrograph of an immunogold-labelled section of tulip virus.

Keywords

DISCDECONVOLUTIONELECTRON MICROGRAPHIMMUNOGOLD LABELLATTICEPIXEL

MSC classification

Primary: 60D05: Geometric probability and stochastic geometry
Secondary: 62M30: Spatial processes
Type
Stochastic Geometry and Statistical Applications
Information
Advances in Applied Probability , Volume 28 , Issue 3 , September 1996 , pp. 627 - 640
Copyright
Copyright © Applied Probability Trust 1996 

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