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2018 | OriginalPaper | Chapter

Projection Selection for Binary Tomographic Reconstruction Using Global Uncertainty

Authors : Gábor Lékó, Péter Balázs, László G. Varga

Published in: Image Analysis and Recognition

Publisher: Springer International Publishing

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Abstract

Binary tomography focuses on the problem of reconstructing homogeneous objects from a small number of their projections. In many applications, incomplete projection data holds insufficient information for the correct reconstruction of the original object. In this paper, we provide an optimization based method to select the “most informative” projection set, using information of global uncertainty. Beside the projection data we assume no further knowledge of the image to be reconstructed. Still, we achieve approximately as accurate reconstruction results, as it is possible to gain with a former method that uses blueprint images to find the optimal set of projections. We give experimental results for validating our approach on artificial images of various structures.

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next chapter Reconstruction of Binary Images with Fixed Number of Strips
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Metadata
Title
Projection Selection for Binary Tomographic Reconstruction Using Global Uncertainty
Authors
Gábor Lékó
Péter Balázs
László G. Varga
Copyright Year
2018
Book
Image Analysis and Recognition

Print ISBN: 978-3-319-92999-6

Electronic ISBN: 978-3-319-93000-8

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-93000-8_1

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