Bayesian learning of material density function by multiple sequential inversions of 2-D images in electron microscopy

dc.contributor.authorChakrabarty, Daliaen
dc.contributor.authorPaul, Shashien
dc.date.accessioned2015-03-25T16:27:09Z
dc.date.available2015-03-25T16:27:09Z
dc.date.issued2015-03-01
dc.description.abstractWe discuss a novel inverse problem in which the data is generated by the sequential contractive projections of the convolution of two unknown functions, both of which we aim to learn. The method is illustrated using an application that relates to the multiple inversions of image data recorded with a scanning electron microscope, with the aim of learning the density of a given material sample and the microscopy correction function. Given the severe logistical difficulties in this application of taking multiple images at different viewing angles, a novel imaging experiment is undertaken, resulting in expansion of information. In lieu of training data, it is noted that the highly discontinuous material density function cannot be modelled using a Gaussian process (GP) as the parametrisation of the required nonstationary covariance function of such a GP cannot be achieved without training data. Consequently, we resort to estimating values of the unknown functions at chosen locations in their domain—locations at which an image data are available. Image data across a range of resolutions lead to multiscale models which we use to estimate material densities from the micrometre to nanometre length scales.We discuss applications of the method in nondestructive learning of material density using simulated metallurgical image data, as well as perform inhomogeneity detection in multicomponent composite on nanometre scales, by inverting real image data of a brick of nanoparticleen
dc.fundern.aen
dc.identifier.citationChakrabarty, D. and Paul, S. (2015) Bayesian learning of material density function by multiple sequential inversions of 2-D images in electron microscopy. In: Polpo A., Louzada F., Rifo L., Stern J., Lauretto M. (eds) Interdisciplinary Bayesian Statistics. Springer Proceedings in Mathematics and Statistics, 118, pp. 35-48en
dc.identifier.doihttps://doi.org/10.1007/978-3-319-12454-4_3
dc.identifier.isbn9783319124537
dc.identifier.issn2194-1009
dc.identifier.urihttp://hdl.handle.net/2086/10840
dc.language.isoenen
dc.peerreviewedYesen
dc.projectidn.aen
dc.publisherSpringer New York LLCen
dc.researchgroupEmerging Technologies Research Centreen
dc.researchinstituteInstitute of Engineering Sciences (IES)en
dc.subjectChemical detectionen
dc.subjectScanning electron microscopyen
dc.subjectBayesian learningen
dc.subjectCorrection functionen
dc.subjectImaging experimentsen
dc.subjectMaterial density functionen
dc.subjectMulticomponent compositesen
dc.subjectMultiple inversionen
dc.subjectNon-stationary covarianceen
dc.subjectSequential inversionen
dc.titleBayesian learning of material density function by multiple sequential inversions of 2-D images in electron microscopyen
dc.typeConferenceen

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