X-ray images are often used to make inferences about physical phenomena and the entities about which inferences are made are complex. The Bayes linear approach is a generalisation of subjective Bayesian analysis suited to uncertainty quantification for complex systems. Therefore, Bayes linear is an appropriate tool for making inferences from X-ray images. In this thesis, I will propose methodology for making inferences about quantities, which may be organised as multivariate random fields. A number of problems will be addressed: anomaly detection, emulation, inverse problem solving and transferable databases. Anomaly detection is deciding whether a new observation belongs to the same population as a reference population, emulation is the...
Many scientific, medical or engineering problems raise the issue of recovering some physical quantit...
Researchers at the Los Alamos National Laboratory (LANL) are interested in quantitatively reconstruc...
International audienceIn this paper, first the basics of the Bayesian inference for linear inverse p...
Abstract X-ray tomography has applications in various industrial fields such as sawmill industry, o...
The Bayes Inference Engine (BIE) is a flexible software tool that allows one to interactively define...
In this work, we describe a Bayesian framework for the X-ray computed tomography (CT) problem in an ...
Funding Information: The work of the first author was supported by the the Academy of Finland throug...
We attack the problem of recovering an image (a function of two variables) from experimentally avail...
An important component of analyzing images quantitatively is modeling image blur due to effects from...
We present a new model for the image-formation processes in a direct x-ray imaging system. The imagi...
Our understanding of physical systems often depends on our ability to match complex computational mo...
Quantitative image analysis in the security sciences formulates an image deblurring problem as a Bay...
This thesis presents novel algorithms in X-ray computed tomography imaging using limited or sparse d...
This article describes basic concepts of statistical estimation based on experimental data, includin...
[ANGLÈS] This project belongs to the field of medical imaging and was motivated by the idea of elabo...
Many scientific, medical or engineering problems raise the issue of recovering some physical quantit...
Researchers at the Los Alamos National Laboratory (LANL) are interested in quantitatively reconstruc...
International audienceIn this paper, first the basics of the Bayesian inference for linear inverse p...
Abstract X-ray tomography has applications in various industrial fields such as sawmill industry, o...
The Bayes Inference Engine (BIE) is a flexible software tool that allows one to interactively define...
In this work, we describe a Bayesian framework for the X-ray computed tomography (CT) problem in an ...
Funding Information: The work of the first author was supported by the the Academy of Finland throug...
We attack the problem of recovering an image (a function of two variables) from experimentally avail...
An important component of analyzing images quantitatively is modeling image blur due to effects from...
We present a new model for the image-formation processes in a direct x-ray imaging system. The imagi...
Our understanding of physical systems often depends on our ability to match complex computational mo...
Quantitative image analysis in the security sciences formulates an image deblurring problem as a Bay...
This thesis presents novel algorithms in X-ray computed tomography imaging using limited or sparse d...
This article describes basic concepts of statistical estimation based on experimental data, includin...
[ANGLÈS] This project belongs to the field of medical imaging and was motivated by the idea of elabo...
Many scientific, medical or engineering problems raise the issue of recovering some physical quantit...
Researchers at the Los Alamos National Laboratory (LANL) are interested in quantitatively reconstruc...
International audienceIn this paper, first the basics of the Bayesian inference for linear inverse p...